The electronic structural evolution of LLM-105 crystal and the (101) plane of LLM-105 crystal under different pressures is investigated by the density functional theory (DFT) calculations and the surface properties of seven low-Miller-index planes of LLM-105 crystal were investigated in this study. The result demonstrates that the surface energy of (101) plane of LLM-105 is much smaller when compared with other low-Miller-index planes of LLM-105.In order to further investigate the stability of crystal structure and (101) plane of LLM-105 under different pressures, a series of pressures ranged from 0 GPa to 55 GPa were applied during the DFT calculation, and the result shows that the electronic structures of LLM-105 crystal and the (101) plane changed with the increase of external pressure and the evolution process of (101) plane is different from that of LLM-105 crystal structure.

Remote sensing technology provides a new way to explore the earth for geoscience applications. In the context of continuous deepening of geological research and the vigorous development of geospatial information science, 3D geological modeling technology has become a research hotspot in the intersection of earth science and information science. The traditional 3D geological modeling technology refers to underground 3D modeling using geological data. This modeling method can only display the underground scene in 3D visualization, but cannot display the surface in detail. In this study, remote sensing technology is adopted to improve the traditional 3D geological modeling method, so that geological modeling can be integrated with remote sensing image, and a new 3D geological modeling method based on remote sensing technology is developed. This method can perform integrated 3D visualization of underground and aboveground scenes, which provides a new way for disaster prevention and reduction, geological prospecting and tectonic interpretation. The new 3D geological modeling method is mainly applied in the geological field, and has made new progress in multi-source heterogeneous geological data fusion, large-scale high-precision modeling, geological grid subdivision, attribute modeling technology, remote sensing image fusion and other aspects. Taking the Ya'an area as an example, this paper makes use of the new generation of 3D geological modeling technology to carry out 3D geological modeling and visual display. The 3D visualization in Ya'an area verifies the feasibility and effectiveness of the new 3D geological modeling method based on remote sensing technology in the current data environment.

Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials databases has fundamentally expanded the choices of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit ZT quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principles calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principles calculations are carried out for narrow-bandgap semiconductors which exhibiting potential thermoelectric application. The present framework of calculations mainly includes deformation potential module, electrical transport performance module, mechanical and thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing searching and viewing the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics.

The biology of aging is focused on the identification of novel pathways that regulate the underlying processes of aging to develop interventions aimed at delaying the onset and progression of chronic diseases to extend lifespan. However, the research on the aging field has been conducted mainly in animal models, yeast, Caenorhabditis elegans and cell culture. Thus, it is unclear to what extent this knowledge is transferable to humans since they might not reflect the complexity of aging in people. Organoid culture is an in vitro 3D cell-culture technology that reproduces the physiological and cellular composition of the tissues and/or organs. This technology is being used in the cancer field to predict the response of a patient-derived tumor to a certain drug or treatment serving as patient stratification and drug-guidance approaches. Modeling aging with patient-derived organoids has a tremendous potential as a preclinical model tool to discover new biomarkers of aging, to predict adverse outcomes during aging and to design personalized approaches for prevention and treatment of aging-related diseases and geriatric syndromes. This could represent a novel approach to study chronological and/or biological aging paving the way to personalized interventions targeting the biology of aging.

The present work investigates primarily the morphology evolution of the body-centered-cubic (BCC)/B2 phases in AlxNiCoFeCr high-entropy alloys (HEAs) with increasing Al content, which has been neglected so far. There exist two types of microscopic morphologies of BCC and B2 phases in this HEA series: one is the weave-like morphology induced by the spinodal decomposition, and the other is the microstructure of a spherical disordered BCC precipitation on the ordered B2 matrix that appears in HEAs with a much higher Al content. The shape of coherent precipitates is found to be closely related to the lattice misfit between BCC and B2 phases, which is sensitive to Al. The mechanical properties, including the compressive yielding strength and microhardness of the AlxNiCoFeCr HEAs, are also discussed in light of the concept of the valence electron concentration (VEC).

The partial discharge (PD) event in high voltage insulation releases energy, exerts mechanical pressure, and generates elastic waves. Detecting and locating these PD events through short-duration acoustic pulses is well-established, particularly in gas-insulated systems and oil-insulated transformers. However, its full potential remains untapped in solid insulation systems, where the propagation capability of acoustic pulse and the acoustic reflections pose fundamental challenges to the Acoustic Emission (AE) detection technique. This study investigates the influence of reflections and multiple paths on the propagating acoustic pulse in polymeric insulating materials using a finite element method (FEM) in COMSOL. It was observed that the reflections from the boundary influence the propagating pulse's shape, peak magnitude, and arrival time. An analytical MATLAB model further quantifies the impact of multiple propagation paths on the shape, magnitude, and arrival time of the pulse travelling in a cylinder. Additionally, a Perfect Matched Layer (PML) was implemented in the COMSOL model to eliminate reflections from the boundary, and it revealed that the acoustic pulse magnitude decreases with distance following the inverse square law. In essence, the models aid in measuring how reflections contribute to the observed signals, facilitating the precise identification of the source of the PD event in the tested system.

To realize precise control of the quantum dots (Qdots) device, multi-channel precision bias source plays the key role. In this paper, the 16-channel high precision bias source with 18-bit resolution for Qdots device was designed. The prototype was made and its performance was tested. The short time fluctuations can reach 50μV. The step response time is less than 3μs. The resolution, stability, linearity and dynamic range of the bias source exhibits good performance. What's more, the bias source can be controlled locally and online. The results show that it is one effective and feasible topology for experiments in Qdots device application.

This article reports a novel and efficient method to synthesize graphene by thermal decomposition process. In this method, silicon carbide (SiC) thin films grown on Si(100) wafers with an AlN buffer layer were used as substrates. A CO₂ laser beam heating without vacuum or controlled atmosphere was applied for SiC thermal decomposition. The physical, chemical, morphological, and electrical properties of the laser-produced graphene were investigated for different laser energy densities. The results demonstrate that graphene was produced in form of small islands with quality, density and properties depending on the applied laser energy density. Furthermore, the produced graphene exhibits a sheet resistance characteristic similar to graphene grown on mono-crystalline SiC wafer, which indicates its potential for electronic device applications.

Leaf area estimation is a critical component in the study of plant growth and productivity within agricultural systems. This research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species: S. quitoense, S. betaceum, P. peruviana, R. fruticosus, P. ligularis and P. edulis. Leveraging response variables such as species type, leaf length and width, the package employs advanced machine learning algorithms to estimate leaf area accurately. The primary focus of the study is to identify the most effective model for describing the relationship between leaf width, length, and area for each plant species. Currently, the LeafArea package utilizes four different machine learning algorithms, namely generalized linear model (GLM), generalized linear mixed model (GLMM), Random Forest and XGBoost. Among these, XGBoost stands out as a top-performing algorithm, exhibiting exceptional predictive accuracy. The evaluation metrics employed in the program provide valuable insights for researchers, aiding in informed decision-making. Specifically, XGBoost demonstrates significantly lower prediction errors and approaches a near-perfect R2 value, emphasizing its potential to enhance predictive accuracy. These results underscore the efficacy of machine learning techniques, as a compelling choice for researchers seeking precise and robust predictions in leaf area estimation. The LeafArea package thus represents a valuable tool for advancing our understanding of plant growth dynamics, resource allocation, and overall productivity within agricultural ecosystems.

This paper introduces a method to achieve optimal Gaussian estimation even when operating with low precision numerical representations of the full covariance matrix, such as 16-bit integer or 32-bit single precision formats, instead of more expensive double precision floating point representations (64 bits). The approximation's numerical error is accurately estimated in a conservative manner, resulting in minimal covariance inflation. When combined with a compressed estimator like the GCKF (Global Compressed Kalman Filter), which performs global updates at low frequency, this approach produces estimates that are nearly identical to the optimal ones. The deviation between the results obtained using this new approach and those obtained with the standard GCKF (which operates in double precision) is negligible. This new method is particularly relevant in high-dimensional estimation problems that require high-frequency operation. By leveraging the compressed operation of the GCKF, the estimation process applies the necessary approximation only during low-frequency global updates, using the single precision format instead of the more expensive double precision representations. The introduced numerical approximation is accurately estimated while maintaining a conservative approach, resulting in minimal covariance inflation. When combined with the GCKF, which performs global updates at low frequency, the estimates produced are almost identical to the optimal ones. This approach is particularly useful in high-dimensional estimation problems that require high-frequency operation. By representing the full covariance matrix in a low precision numerical format and applying appropriate scaling, memory usage is significantly reduced, offering substantial benefits for high-dimensional estimation problems and enabling real-time smoothing processes in such cases. By reducing the memory requirements for storing the full covariance matrix, the CPU avoids encountering a high number of page faults, resulting in improved processing performance. Lastly, the experimental section verifies the performance of the proposed approach in a single precision (32-bit floating point) GCKF SLAM process, assessing expected values and standard deviations of marginal PDFs.

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of a cancer or transformed cell, the initiation and progression were shown to be driven by only a few mutational events viz. driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient’s cancer cell gained large attention recently for designing better targeting therapies towards paving way for the precision cancer medicine. With rolling advancements in high-throughput omics technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures, and their potential application in developing precision cancer therapies.Keywords:

A novel method based on a hybrid incremental modeling approach has been designed and applied to imbalance detection in ultra-high precision rotating machines. The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearest-neighbour) to take advantage of their complementary capacities. Three normalization strategies of evaluating the effect on accuracy are analyzed. A comparative study demonstrates that the hybrid incremental model provides better error-based performance indices for detecting imbalance than a nonlinear regression model and an adaptive neural-fuzzy inference system model. The suitability of Mahanolobis normalization for hybrid incremental modeling is also demonstrated in this case study. The proposed strategy for imbalance detection is simple, fast, and non-intrusive, reducing the deterioration in the performance of ultra-high precision rotating machines due to vibrations.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

The purpose of this study was to conduct a meta-analysis that shows the influence of fiber on ultimate compressive strength and tensile strength of ultra-high performance concrete. The internet scholarly search engines and ScienceDirect article references were used to illustrate the papers concerning the experimental investigations of mechanical properties of ultra-high strength concrete with and without fiber with clearly, completely and comparative raw data. The normal concrete test results were dismissed from this search. Seven trials were identified based on the adopted inclusion and exclusion criteria above. The meta-analysis based on standardized mean difference was carried out on the basis of a fixed-effects model for the major outcomes of the ultimate compressive and tensile properties of ultra-high performance concrete. A total of 888 test specimens were enrolled in these seven trials. The combined analysis yielded a sign of a significant improvement in ultimate compressive strength and tensile strength of ultra-high strength concrete with fiber addition of 2% by concrete volume. The summary effect size of ultimate compressive strength was 2.34 while a more improvement in term of tensile strength with effect size of 2.64. By addition fiber of 2% provides a significant benefit in mechanical properties of ultra-high performance concrete.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

Diarylethene is a prototypical molecular switch that can be reversibly photoisomerized between its open and closed forms. Ligands bpy-DAE-bpy, consisting of a phenyl-diarylethene-phenyl (DAE) central core and bipyridine (bpy) terminal substituents, are able to self-organize. They are investigated by scanning tunneling microscopy at the solid-liquid interface. Upon light irradiation, cooperative photochromic switching of the ligands is recognized down to the sub-molecular level. The closed isomers show different electron density of states (DOS) contrasts, attributed to the HOMO or LUMO molecular orbitals observed. More importantly, the LUMO images show remarkable differences between the open and closed isomers, attributed to combined topographic and electronic contrasts mainly on the DAE moieties. The electronic contrasts from multiple HOMO or LUMO distributions, combined with topographic distortion of the open or closed DAE, are interpreted by density functional theory (DFT) calculations.

China is facing the development requirements of the dual carbon goal and the construction needs of resilient cities. Currently, high-strength composite structural systems are a feasible solution, but their research and application in the construction field are insufficient, especially for the combination forms of concrete above C100 and steel above Q550. In response to this issue, this paper proposes a new high-performance structural system, namely the composite frame - high-strength steel plate wall core tube resilient structure system, which includes high-strength steel plate (Q550) - ultra-high strength concrete (C100) shear walls, replaceable energy dissipation coupling beams, and composite frames. Taking a 200 meter building as an example, this paper designs and establishes calculation models for a conventional reinforced concrete frame-core tube structure and a high-performance structure based on PKPM software, and establishes elastoplastic analysis models for both based on SAUSAGE software, and then conducts dynamic elastoplastic time history analysis and seismic resilience assessment of structures under design basis earthquakes (DBEs), maximum considered earthquake (MCEs) and extremely rare earthquakes (EREs). Research has shown that compared to conventional structures, new high-performance structures can effectively reduce the size of shear walls, reduce the self-weight of the structure, and enhance the space for building use; The story drift ratio has higher redundancy than specification limits, lower plastic damage and overall stiffness degradation of the structure, and better seismic performance; The seismic resilience has significantly improved, especially in the case of casualties, which can better ensure the safety of people's lives and property.

Ovarian cancer (OC) is an umbrella term for cancerous malignancies affecting the ovaries, yet treatment options for all subtypes are predominantly derived from high-grade serous ovarian cancer, the largest subgroup. The concept of "functional precision medicine" involves gaining personalized insights on therapy choice, based on direct exposure of patient tissues to drugs. This especially holds promise for rare subtypes like low-grade serous ovarian cancer (LGSOC). This study aims to establish an in vivo model for LGSOC using zebrafish embryos, comparing treatment responses previously observed in mouse PDX models, cell lines, and 3D tumor models. To address this goal, a well characterized patient-derived LGSOC cell line with the KRAS mutation c.35 G > T (p.(Gly12Val)) was used. Fluorescently labelled tumor cells were injected into the perivitelline space of 2 days post fertilization zebrafish embryos. At 1 day post-injection xenografts were as-sessed for tumor size, followed by random allocation into treatment groups with trametinib, luminespib, and trametinib + luminespib. Subsequently xenografts were euthanized and analysed for apoptosis and proliferation by confocal microscopy. Tumor cells formed compact tumor masses (n=84) in vivo and with clear Ki67 staining, indicating proliferation. Zebrafish xenografts exhibited sensitivity to trametinib and luminespib, individually or combined, within a two-week period, establishing them as a rapid and complementary tool to existing in vitro and in vivo models for evaluating targeted therapies in LGSOC.

By combing continuum topology optimization (TO) method and lattice structure technique, a sandwich aircraft spoiler with a high stiffness-to-weight is designed. TO method is served to produce the shell of the aircraft spoiler and the lattice structure, used as cores, is employed to support the shell. TO problem is established as maximizing the stiffness of the structure with limited material volume. Density-based method is utilized to achieve a 0/1 solution. We then empirically replace the core of the aircraft spoiler by using 3D kagome lattice structure. Two different materials, i.e., aluminum alloy and titanium alloy, are synthetically applied to further reduce the weight and simultaneously improve the strength of the aircraft spoiler. Numerical simulations are conducted to show that the designed aircraft spoiler can meet the service environment with a reduction of its weight by approximately 80% when compared with that of the initial design model. Finally, we have fabricated the designed model with photosensitive resin by using 3D printing technique.

Ultrasonic imaging & NDE applications can greatly improve their signal-to-noise ratios (SNR) by driving each transducer (composing piezoelectric arrays) with a spike giving pulsed power of k-Watts, repetitively at a PRF = 5000 spikes/s, by using a HV capacitive-discharge generator. However very-high levels, of pulsed intensities (3-10 A) and voltages (300-700 V) must be considered for a rigorous spike modeling. Even though the consumed "average" power will be small, the intensity through each transducer achieves several amperes, so the pulsed powers delivered by each HV generator can attain levels higher than in CW high-power ultrasonic applications: e.g., up to 5 kW / spike. This is concluded here from a transient modeling of the loaded generator. Then, unforeseen phenomena rise: intense brief pulses of driving power & emitted force in transducers, and non-linearities in driver semiconductors, because their characteristic curves only include linear ranges. But fortunately, piezoelectric devices working in this intense regime do not show serious heating problems, because the average power remains being moderate. Intensity, power and voltage, driving a broadband transducer from a HV capacitive pulser, are calculated to drastically improve (in ≅ 40 dB) the ultrasonic net dynamic range available, with emitted forces ≅ 250 Newtons pp and E/R received pulses of 70 V pp.

Phenotypic characterization of crop genotypes is an essential yet challenging aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agriculture research due to diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. Phenotypic traits of crop fresh biomass, dry biomass, and plant height estimated by CBM data had high correlation with ground truth manual measurements in wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

By means of density functional theory (DFT) calculations, we have investigated the effect of hydrostatic pressure on the structural, electronic and elastic properties of the barium-doped silicon clathrate Ba8Si46 in the type-VIII structure (α phase). Physical properties are calculated under different conditions of pressure (0 GPa to 45 GPa) using the GGA-PBE functional, those calculations have been performed using the Cambridge serial total energy package CASTEP code within the Materials Studio package. Electronic properties have shown that the type-VIII Ba8Si46 has metal-like properties with a fundamental bandgap of 1 eV. Under pressure the fundamental bandgap increases slightly and the positions of the valence band maximum VBM and the conduction band minimum CBM remain unchanged. We found that the compound is mechanically stable under the pressure range, but this needs to be confirmed experimentally through synthesis, a comparison with the type-I and the guest-free counterparts has exhibited promising features for the type-VIII Ba8Si46.

Towns emerged as dynamic economic and political centers during the Middle Ages, giving rise to the emergence of new social classes. As a result of these functions, a new relationship began to be forged with the rural world, which supplied towns with foodstuffs that satisfied new social demands. Archaeobotanical analysis (carpology) allows us to understand the flow of cash crops by tracing seeds and fruits produced in the countryside that were consumed in and redistributed from the towns. The study of waterlogged contexts from medieval archaeological sites in the Kingdom of Galicia (Santiago de Compostela, Padrón, and Pontevedra) has provided a set of species that played a crucial role in the economy of the urban dwellers and that possibly were related to differential access or food preferences. Evidence for fruits (grapes, chestnuts, figs, apples, and cherries, among others), garden crops (melon), and cereals (foxtail millet, rye, naked wheat, and oat) has been documented. Broomcorn millet is particularly abundant, demonstrating that it was important for subsistence. Some of the species found (medlar and turnip/grelo) are novel in the archaeobotanical literature of the medieval period in the Iberian Peninsula.

By means of X-ray computed microtomography (XCMT) the existence of a developed microporous structure having an average pore diameter of ~3.5 μm and pore content of ~1.1 vol.% has been revealed in unirradiated polytetrafluoroethylene (PTFE). It has been found that the combined action of gamma radiation (absorbed dose per PTFE of ~170 kGy) and high temperatures (327-350 °C) leads to the disappearance of the microporous structure and the formation of several large pores with sizes from 30 to 50 μm in the bulk of thermal-radiation modified PTFE (TRM-PTFE). It has been established by X-ray diffraction (XRD) analysis that the thermal-radiation modification of PTFE leads to an increase in the interplanar spacings, the degree of crystallinity and volume of the unit cell, as well as to a decrease in the size of crystals and the X-ray density of the crystalline phase in comparison with the initial polymer. It is assumed that the previously established effect of improving the deformation-strength and tribological properties of the TRM-PTFE can be due not only to the radiation cross-linking of polymer chains but also to the disappearance of the micropore system and to the ordering of the crystalline phase of PTFE.

To reduce the carbon emissions during heating in the manufacturing process, microwaves have attracted significant attention. Microwave has a lot of advantages rather than traditional heating method such as rapid heating, lower thermal damage and eco-friendly process. In order to apply microwaves to manufacturing process, uniform and efficient heating is required. We have analyzed the effect of various design parameters such as cavity heights, the application of the reflector, and the number and positions of waveguides for uniform and efficient heating by numerical simulation and verified that by experiment. The results showed that a slight change in the cavity height altered the electromagnetic field distribution and heating parameters, such as the coefficient of variance and power absorption efficiency. With reflectors installed, uniform heating was achieved and power absorption was improved, with the spherical reflector showing the maximum efficiency. The use of double waveguides heated the target material in a uniform manner. An increase in the power supply also led to uniform heating. This large-scale analysis will be helpful in applying microwaves to actual industrial sites.

This paper analyses the viability of different solutions to passively augment the axial stiffness of a horizontal axis radial levitation passive magnetic bearing (PMB) with topology previously studied. The zero-field cooling (ZFC) of high-temperature superconductor (HTS) bulks promotes higher magnetic impulsion and levitation forces and lower electromagnetic losses than with the field-cooling (FC), but on the other hand, the guiding stability is much lower than with FC. Because of stability reasons, FC was adopted in most superconducting maglev systems. The trend of this research group has been to develop an HTS ZFC horizontal axis radial levitation PMB presenting notable levitation forces with reduced electromagnetic losses, defined by a topology that creates guiding stability. Previous work has shown that even optimizing the bearing geometry to maximize magnetic guidance forces, could not be enough to guarantee the axial stiffness required for many applications. First, the extent to which guidance forces are augmented by increasing the number of HTS bulks in the stator is evaluated. Then, the axial stiffness augmentation by passively adding two limiting permanent magnet (PM) rings is evaluated. Results show that the axial stiffness is highly augmented by adding limiting PM rings with no significant additional investment. This change enables using the studied ZFC superconducting PMB in high-precision axial stability applications, such as precision gyroscopes, horizontal axis propellers and turbines.

Rockburst is a hazardous phenomenon in deep tunnels influenced by geological structural planes like faults, joints, and shear planes. Small-scale shear-plane-like structures have damaging impact on the boundaries of the tunnel, which accumulate high stresses. Such a shear plane was exposed in the side wall of the right headrace tunnel in the Neelum-Jehlum Hydropower Project. This project is constructed in the tectonically active Himalayas with high stress conditions. A shear plane combined with high stress conditions is very dangerous in deep excavations. The influence of a shear zone on rockburst occurrence near a tunnel is studied. The FLAC3D explicit code simulated the shear zone in the right tunnel, revealing that the stresses are concentrated near the shear zone, with no stress concentration in the left tunnel, which has neither shear zone nor rockburst. Rock mass was displaced near this shear zone even before excavation. Modeling results confirm that the side wall shear zone of the tunnel has a major influence on rockburst occurrence. A shear slip along this plane released huge amounts of energy, causing human fatalities and property damage. A numerical simulation validates the actual conditions and helps us understand the phenomenon of stress concentration near the shear zone and its impact during deep tunneling.

We conducted an in-situ crystal structure analysis of ferroselite at non-ambient conditions. The aim is to provide a solid ground to further the understanding of the properties of this material in a broad range of conditions. Ferroselite, marcasite-type FeSe2, was studied under high pressures up to 46 GPa and low temperatures, down to 50 K using single-crystal microdiffraction techniques. High pressure and low temperatures were generated using a diamond anvil cell and a cryostat. We found no evidences of structural instability in the explored P-T space. The deformation of the orthorhombic lattice is slightly anisotropic. As expected, the compressibility of the Se-Se dumbbell, the longer bond in the structure, is larger than that of the Fe-Se bonds. Less obvious is the behavior of the octahedral bonds, the shorter bond is the most compressible determining a small increase in the octahedron distortion with pressure. We also achieved a robust structural analysis of ferroselite at low temperature in the diamond anvil cell. Structural changes upon temperature decrease are small but qualitatively similar to those produced by pressure.

As one of the most promising ablative fillers, multi-walled carbon nanotubes (MWCNTs) have been used to improve the ablative resistance of Ethylene-Propylene-Diene Monomer (EPDM) insulators by facilitating the carbothermal reduction reaction of silica. However, the contribution of MWCNTs to char layer structure of the insulators was unclear. In this work, the effects of MWCNTs on char layer structure and ablative resistance were investigated in different EPDM-based insulators with and without silica. The results showed that adding only 3 phr MWCNTs can reduce the linear ablation rate of EPDM-based insulators without silica by 31.7%, while 6 phr MWCNTs are required to obtain similar results in EPDM-based insulators with silica. Both the char layer morphology of the two insulators gradually evolved into dense porous structure as MWCNTs content increasing, but their formation mechanisms are different. The XRD and Raman spectrum showed that different physicochemical reactions occurred around MWCNTs under different charring components. The proposed ablation mechanism was further verified by designing alternating multilayer distribution of MWCNTs and silica. This work can guide the construction of desirable char layer structure for increasing ablative resistance of EPDM-based insulators.

High resolution information on nucleotide arrangement in the genome of any or-ganism is critical to understand its genetic makeup, function, and evolutionary his-tory. For years, DNA sequencing has provided crucial breakthroughs in the field of genomics and molecular biology. With the advent of technology, Next Generation Sequencing has now transformed the whole genome sequencing prospects, thus unveiling the novel and interesting features of an organism genome that can behold. Next generation sequencing platforms offer realistic approach to identify and anal-yses whole genome with high throughput, accuracy, scalability in a very short time span. Information obtained can be further used in improving our existing healthcare systems, designing novel and personalized therapeutic modules, making the envi-ronment sustainable and proactively preparing ourselves for future pandemics and outbreaks. This article discusses the various NGS technologies and approaches, their advantages, drawbacks, and futuristic usage in different fields. Though still in nascent stages and marred with flaws, with ongoing advancement, NGS will indeed have promising impacts in the future.

Abstract: : 2,3-dihydro-5,6,7,8-tetranitro-1,4-benzodioxine (TNBD), molecular formula =C8H4N4O10 is a completely nitrated in aromatic ring 1,4-benzodioxane derivative. The con-venient method of TNBD synthesis was developed (yield=81 %). The detailed structure of this compound has been investigated by X-ray crystallography. The results of differential thermal analysis (DTA) obtained with twice re-crystallized material showed onset at 247 and isoterm at 288 0C, with further total degradation at 300 0C. The experimental density 1,85 g/cm3 of TNBD was determined by X-ray crystallography. The spectral properties of TNBD (NMR, FT-IR, Ra-man) were explored. The detonation properties of TNBD calculated by EXPLO 5 code was slightly superior in comparison to standard high energy material - tetryl (detonation velocity of TNBD - 7727 m/s; detonation pressure - 278 kbar; tetryl – 7570 m/s and 226.4 kbar at 1.614 g/cm3, or 260 kbar at higher density - 1.71 g/cm3. The obtained preliminary results might suggest TNBD can be a potential thermostable high energy and density material (HEDM).

The flow stress of face-centered cubic (FCC) metals exhibits a rapid increase near a strain rate of 104 s-1 under fixed strain conditions. However, many existing constitutive models either fail to capture the mechanical characteristics of this plastic deformation or use piecewise strain rate hardening models to describe this phenomenon. Unfortunately, the piecewise models may suffer from issues such as discontinuity of physical quantities and difficulties in determining segment markers, and struggling to reflect the underlying physical mechanisms that give rise to this mutation phenomenon. In light of this, this paper proposes that the abrupt change in flow stress sensitivity to strain rate in FCC metals can be attributed to microstructural evolution characteristics. To address this, a continuous semi-empirical physical constitutive model for FCC metals is established based on the microstructural size evolution proposed by Molinari-Ravichandran and the dislocation motion slip mechanism. This model effectively describes the mutation behavior of strain rate sensitivity under fixed strain, particularly evident in the annealed OFHC. The predicted results of the model across a wide range of strain rates (10-4 − 106 s-1) and temperatures (77 − 1096 K) demonstrate relative errors generally within ±10% of the experimental values. Furthermore, the model is compared to five other models, including Mechanical Threshold Stress (MTS), Nemat-Nasser-Li (NNL), Preston-Tonks-Wallace (PTW), Johnson-Cook (JC), and Molinari-Ravichandran (MR) models. A comprehensive illustration of errors reveals that the proposed model outperforms the other five models in describing the plastic deformation behavior of OFHC. The error results offer valuable insights for selecting appropriate models for engineering applications and provide significant contributions to the field.

The three-dimensional single fin configuration finds application in an intake geometry where the cowl-shock wave interacts with the side-wall boundary-layer. Accurate numerical simulation of such three-dimensional shock/turbulent boundary-layer interaction flows, which are characterized by the appearance of strong crossflow separation, is a challenging task. Reynolds-averaged Navier-Stokes computations using the shock-unsteadiness modified Spalart-Allmaras model is carried out at Mach of 5 at large fin angle of 23^◦. The computed results using the modified model are compared to the standard Spalart-Allmaras model and validated against the experimental data. The focus of work is to implement the modified model and to study the flow physics in detail in the complex region of swept-shock-wave turbulent boundary-layer interaction in terms of the shock structure, expansion fan, shear layer and the surface streamlines. The flow structure is correlated to the wall pressure and skin friction in detail. It is observed that the standard model predicts an initial pressure location downstream of the experiments. The modified model reduces the eddy viscosity at the shock and predicts close to the experiments. Overall, the surface pressure using modified model is predicted accurately at all the locations. The skin friction is under predicted by both the models in the reattachment region and is attributed to the poor performance of turbulence models due to flow laminarization.

Cavitation resulting from underwater explosions in compressible multiphase or multicomp-onent flows presents significant challenges due to the dynamic nature of shock-cavitation-structure interactions, as well as the complex and discontinuous nature of the involved interfaces. Achieving accurate resolution of interfaces between different phases or components, in the presence of shocks, cavitating regions, and structural interactions, is crucial for modeling such problems. Furthermore, pressure convergence in simulations involving shock-cavitation-structure interactions requires accurate algorithms. In this research paper, we employ the diffuse interface method, also known as the interface capturing scheme, to investigate cavitation in various underwater explosion test cases near different surfaces; free surface and a rigid surface. The simulations are conducted using the Unstructured Compressible Navier-Stokes (UCNS3D) finite volume framework employing central-weighted essentially non-oscillatory (CWENO) reconstruction schemes, utilising the five-equation diffuse interface family of methods. Quantitative comparisons are made between the performance of both models. Additionally, we examine the effects of cavitation as a secondary loading source on structures, and evaluate the ability of the CWENO schemes to accurately capture and resolve material interfaces between fluids with minimal numerical dissipation or smearing. The results are compared with existing high-order methods and experimental data, where possible, to demonstrate the robustness of the CWENO schemes in simulating cavitation bubble dynamics, as well as their limitations within the current implementation of interface capturing.

The aeroelastic response of lightweight low-speed aircraft with slender wings under extreme flow turbulence intensity is not well-understood. Experiments on a commmercial unmanned aerial vehicle (UAV) with a 3-meter wingspan and aspect ratio of 13.6 were performed in a large open-return wind tunnel with extreme flow turbulence intensity of T∞≈10%. The structural dynamics of the wing in the bending mode and the flow beneath the wing to capture the effect of aileron deflection was measured using laser displacement sensors and tomographic particle image velocimetry (PIV) techniques. The unsteady lift produced by the wing was also measured using a high-capacity load cell at an angle of attack, α of 2 degrees for three freestream velocities U∞ of 13.4 m/s, 17.9 m/s, and 26.8 m/s representing the UAV’s stall through cruise speed. It was found that high flow turbulence intensity with large integral length scales relative to the wing chord plays a dominant role in the large unsteady lift and wing displacements measured. The power spectral density (PSD) of wing structural vibration shows that flow shedding from the wing and the integral length scales have a significant impact on the overall power inherent in the bending vibration of the wing. Computation of vorticity iso-surfaces in the flow measurement volume surrounding the aileron reveal a striking observation; aileron deflection, δa of 10∘becomes less effective in producing additional spanwise vorticity, which is proportional to circulation and lift, at U∞ of 26.8 m/s because the freestream already has elevated levels of vorticity. A paradigm shift in design is suggested for light aircraft structures with slender wings operating in highly turbulent flow, which is to employ multiple control surfaces in order to respond to this flow and mitigate large bending or torsion displacements and the probability of structural failure.

In this paper, a 100CMM regenerative thermal oxidizer (RTO) system based on a high heating device for low-emission combustion is implemented at a volatile organic compound (VOCs) gas generation workplace. Existing RTO has a structure that repeats the inflow and discharge of VOCs gas into a cylindrical drum-type rotating disk and has excellent energy efficiency with a heat recovery rate of 95% or more. However, when used for a long time, there is a problem that rotation stops or explodes due to the discharge of pollutants due to dust and thermal expansion due to high temperature of 800℃. To overcome this, a RTO based on a high heating device was developed to reduce low-emission combustion emissions and energy use. Through 177 hours of demonstration time, the oxidation efficiency of VOCs was 98.87%; the waste heat recovery rate was reduced by 95.78%, the fuel consumption was reduced by 21.95%, and the nitrogen oxide concentration was 3.9 ppm

Based on panel data of 31 provincial-level administrative units (excluding Hong Kong, Macao and Taiwan) in China from 2015 to 2019, using fixed effect model and mediation effect model, this paper makes an empirical analysis on the interaction among digital economy, industrial structure upgrade and high-quality economic development. The results show that, from the overall effect, the digital economy can significantly promote the high-quality development of the economy, and there are regular regional differences, and the relationship between the promotion intensity and the development level of the digital economy is proportional. From the mediation effect, in the relationship between digital economy and high-quality economic development, the upgrade of industrial structure has played a significant part of the mediation effect, the proportion of mediation effect is 25.17% . In addition, the mediation effect of industrial structure upgrade has obvious regional difference, the western area is the strongest, the eastern area is the second, the central area is the weakest. Based on the above research,the concrete policy suggestions are given to promote the development of digital economy and upgrade the level of industrial structure to promote the high-quality development of economy.

Nowadays, there are implemented devices whose purpose is to perform massive computations by saving resources at the time they reduce the latency of arithmetic operations. These devices are usually GPUs, FPGAs and other specialised devices such as "Coral". Neural networks, digital filters and numerical simulators take advantage of the massively parallel operations of such devices. One way to reduce the amount of resources used is to limit the size of the registers that store data. This has led to the proliferation of numeric formats with a length of less than 32 bits, known as short floating point or SFP. We have developed several SFP’s for use in our neural network accelerator design, allowing for different levels of accuracy. We use a 16-bit format for data transfer and different formats can be used simultaneously for internal operations. The internal operations can be performed in 16-bit, 20-bit and 24-bit. The use of registers larger than 16-bit allows the preservation of fractional information while increasing precision. By leveraging some of the FPGA’s arithmetic resources, our design outperforms designs implemented from scratch and is competitive with specialized arithmetic circuits already implemented in the FPGA.

This work deals with the performance evaluation of novel flat photovoltaic-thermal (PV-T) modules under vacuum. Through a 1D (dimensional) steady-state-energy-balance numerical model developed in MATLAB, two different layouts are studied: the first consisting of a photovoltaic (PV) cell installed just below the glass encapsulating the flat panel, and the second where the PV cell is placed on the selective solar absorber (SSA). In both cases the thermal and electrical efficiencies have been evaluated at different SSA operating temperatures, in the range of 323 K to 423 K. The analysis has been conducted at different energy bandgap (E_bg) of the PV cell and assuming a variable transmittance or emittance of the PV cell, depending on the design. The two systems efficiency comparison has been carried out at the same operating temperature. Overall, this work highlights the importance of high vacuum insulation, which guarantees the reduction of convective thermal losses, and shows that the maximum energy is produced for PV cells with E_bg ≈1.5-1.7 eV, depending on layout and operating temperature, by including the thermal output in the PV-T optimization. The energy and exergy efficiencies obtainable using the proposed PV-T systems are considerably improved compared to the results previously reported in the literature.

Ontological premises have profound theoretical implications. This analysis posits a single, universal structure composed of two ontic entities: discrete, quantized units of space ("Planck Spheres") and a State of Absolute Nothingness (the "SOAN"). Planck Spheres and the SOAN form a physical, ultra-high (3 x N) dimensional space ("Planck Space") and a discrete 3D space (or "4D spacetime"). Together, Planck Space and 4D spacetime form a tightly integrated ((3 x N) + 3) hyperspace (the "Dual Ontology"). Critically, a one-to-one mapping and identity exists between the Planck Spheres comprising 4D spacetime and Planck Space. The Dual Ontology conjecture supports radically simplified physical explanations for three distinct areas of theoretical physics: First, the conjecture reconciles fundamental issues in Special Relativity and quantum mechanics by explaining why single and N-body quantum states (a) dynamically evolve in 4D spacetime subject to the speed of light and (b) instantaneously collapse in a physical Planck Space that is not subject to Special Relativity. Second, the temporal and physical asymmetry between the dynamic evolution of quantum states in 4D spacetime subject to Special Relativity and the instantaneous collapse of quantum states in a Planck Space where Special Relativity is inapplicable explains quantum path irreversibility and the arrow of time. Third, at or near the heat death of 4D spacetime, the instantaneous collapse of the energy content of Planck Space and the simultaneous transition of 4D spacetime's extremely widely dispersed energy content to a non-singular, generally localized volume at t = 0 explains 4D spacetime's isotropy, homogeneity, extremely high energy, pressure, and temperature, flatness, and low gravitational entropy. It also explains the horizon and fine-tuning problems.

The novel coronavirus (COVID-19) was first reported in the U.S. on December 29, 2019 and has spread rapidly throughout the country, affecting individuals with varying severity due to their risk status. According to the Centers for Disease Control and Prevention, it is estimated that 45.4% of US adults are at higher risk for complications from coronavirus disease because of cardiovascular disease, diabetes, respiratory disease, hypertension, or cancer. In this study, we developed a mathematical model to assess the impact of a COVID-19 vaccine among low and high risk groups. Numerical simulations shows vaccinating both low and high risk groups simultaneously, rather than prioritizing the vaccine on high risk group only, further reduces the daily mortality. The result supports the need for an aggressive vaccination program, regardless of whether individuals are within the low or high risk population.

Abstract: An Al-Co-Y diffusion coating was prepared on a TiAl alloy by pack cementation process at a low temperature range of 650-720 ℃. The influence of different activators and co-deposition temperatures on the coating’s microstructure and formation process, and high temperature oxida-tion performance was studied. The results showed that NaF was a more suitable activator for preparing the Al-Co-Y diffusion coating on TiAl alloy compared to NH4Cl and AlCl3. A more compact coating structure can be obtained when deposition was performed at 680 ℃ using NaF as the activator. The coating was mainly composed of a TiAl3 outer layer, a TiAl2 inner layer, and a thin Al-rich TiAl inter-diffusion layer. The growth of the coating exhibited a positive linear correlation between coating thickness increase and the square root of deposition time. The high-temperature oxidation tests showed that a dense scale mainly composed of Al2O3 and a small amount of TiO2 formed on the coating after oxidation at 950 ℃ for 100 h. The scale possessed excellent high-temperature oxidation resistance, with a parabolic rate constant of the oxidation weight gain approximately 4.9×10-3 mg2/cm4 h, which is lower than that of the TiAl substrate by about three orders of magnitude, and lower than that of the pure Al diffusion coating more than one order of magnitude.

Pharmacogenetics (PGx) aims to identify the genetic factors that determine inter-individual differences in response to drug treatment maximizing efficacy while decreasing the risk of adverse events. Estimating the prevalence of PGx variants involved in drug response, is a critical preparatory step for large-scale implementation of a personalized medicine program in a target population. Here, we profiled pharmacogenetic variation in fourteen clinically relevant genes in a representative sample set of 1,577 unrelated sequenced Sardinians -- an ancient island population that well accounts for genetic variation in Europe as a whole and, at the same time are enriched in genetic variants that are very rare elsewhere. To this end, we used PGxPOP, a PGx allele caller based on the guidelines created by the Clinical Pharmacogenetics Implementation Consortium (CPIC), to identify the main phenotypes associated with the PGx alleles most represented in Sardinians. We estimate that 99.43% of Sardinian individuals may potentially respond atypically to at least one drug, that on average each individual is expected to have an abnormal response to about 17 drugs, and that for 27 drugs the fraction of the population at risk of atypical responses to therapy is more than 40%. Finally, we highlighted 174 pharmacogenetic variants for which the minor allele frequency is at least 10% higher among Sardinians as compared to other European populations, a fact that may contribute to substantial interpopulation variability in drug response phenotypes. This study provides baseline information for further large-scale pharmacogenomic investigations in the Sardinian population and underlines the importance of the PGx characterization of diverse than European populations as Sardinians.

Lithium titanates are used in various applications, such as anode materials for lithium intercalation (Li4Ti5O12) or breeding materials in fusion reactors (Li2TiO3). Here, we report the formation of nano-crystalline lithium titanates by a mechanochemical approach and present a deeper insight into their structural characteristics by PXRD and solid-state NMR spectroscopy. The compounds were synthesized in a high energy planetary ball mill with varying milling parameters and different grinding tools. NaCl type Li2TiO3 (α-Li2TiO3) was formed by dry milling of lithium hydroxide with titania (rutile or anatase) and by a milling induced structure transformation of monoclinic β Li2TiO3 or spinel type Li4Ti5O12. Heating of mechanochemical prepared α Li2TiO3 induces a phase transformation to the monoclinic phase similar to hydrothermal reaction products, but a higher thermal stability was observed for the mechanochemical formed product. Microstructure and crystallographic structure were characterized by PXRD via Rietveld analysis. Detailed phase analysis shows the formation of the cubic phase from the various educts. A set of two lattice parameters for α Li2TiO3 was refined, depending on the presence of OH- during the milling process. An average crystallite size of less than 15 nm was observed for the mechanochemical generated products. The local Li environment detected by 6Li SPE MAS NMR revealed Li defects in the form of tetrahedral instead of octahedral site occupation. Subsequent adjustment of the structural model for Rietveld refinement leads to better fits, supporting this interpretation.

Recently, due to the decrease in labor force, increase in labor costs, and the desire for improved quality of life, research on robots has been actively conducted to address these issues. However, it is currently difficult to find robots that physically interact with humans. The reason is that the actuators of robots do not have high torque density on their own. To solve this problem, high torque density motors, such as proprioceptive actuators, are being researched. However, the torque density is still insufficient for physical interaction with humans, so we have developed a motor with higher torque density. However, high torque density motors have the disadvantage of lower speed characteristics due to increased Back-EMF. Therefore, to address the deterioration of speed characteristics in the developed motor, we applied the Independent 3-Phase winding structure to improve the speed characteristics. Consequently, through comparison with Y-Connection and Delta-Connection, we propose the most suitable winding structure for high torque density motors intended for physical interaction with humans.

Prolonged high-intensity grinding can modify the crystal structure of solid substances and/or induce chemical reaction, which is referred to as mechanochemical reaction. Such reactions can exert positive influences on hydrometallurgical processes, therefore, many researchers have applied mechanochemical reactions for metals dissolution from minerals. The mechanism of mechanochemical reaction has been investigated using solid analyses and simulations. Structural changes caused by mechanochemical reaction are not yet sufficiently clarified because the ground samples are amorphous. The objective of this study was to analyze structural changes of cerianite in weathered residual rare earth ore by mechanochemical reduction. Structural change was analyzed by x-ray absorption near-edge structure and extended x-ray absorption fine structure analysis at the cerium L_III- and K-edges. These analyses revealed that the structural change of cerianite in this ore induced by mechanochemical reduction involved oxygen vacancy production. The process of the oxygen vacancy formation was closely coupled with the quantum effect of localization–delocalization of the 4f electron of cerium.

Space-based gravitational wave detection is extremely sensitive to disturbances. The Keplerian configuration cannot accurately reflect the variations in spacecraft configuration. Planetary gravitational disturbances are one of the main sources. Numerical simulation is an effective method to investigate the impact of perturbation on spacecraft orbits. This study shows that, in the context of the Taiji project, Earth’s gravity is an essential factor in the change in heliocentric formation configuration, contributing to the relative acceleration between spacecrafts in the order of O(10−6)m·s−2. Considering 00:00:00 on October 27, 2032 as the initial orbiting moment, under the influence of Earth’s gravitational perturbation, the maximum relative change in armlengths and variation rates of armlengths for Taiji is 1.6×105km, 32m·s−1 respectively, compared with the unperturbed Keplerian orbit. Additionally, by considering the gravitational perturbations of Venus and Jupiter, the armlength and relative velocity for Taiji are reduced by 16.01% and 17.45%, respectively, compared with when only considering that of Earth. The maximum amplitude of the formation motion indicator changes with the orbit entry time. Results show that the relative velocity increase between the spacecrafts is minimal when the initial orbital moment occurs in July. Moreover, the numerical simulation results are inconsistent when using different ephemerises. The differences between ephemeris DE440 and DE430 are smaller than those between DE440 and DE421.

Automated extraction of buildings from earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, policy making. Combining data from different sensors such as high-resolution multispectral (HRI) and light detection and ranging (LiDAR) has shown great potential in building extraction. Deep learning (DL) is increasingly used in multimodal data fusion and urban object extraction. However, DL-based multimodal fusion networks may underperform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multimodal features. Recently, an hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset including co-located Very-High-Resolution (VHR) optical images and Light Detection And Ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model overparametrization, which inevitably leads to long training times and heavy computational load. In this paper the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

Standards are not only crucial for obtaining a certification for practical use, but also an essential guidance for inferring how and what to simulate as well test experimentally. This paper examines the potential applicability of existing standards from various domains, including aerospace and automotive, to Wing-Structure- Integrated high-pressure Hydrogen Tanks (SWITHs). It highlights the associated challenges and uncertainties, acknowledging the absence of a single, all-encompassing standard for SWITHs. We embark on an exploration aimed at identifying alternative standards that are best suited for SWITHs. The analysis scrutinizes existing standards that could potentially facilitate SWITH certification, with a particular focus on the inclusivity of externally loaded hydrogen tanks. Assessment criteria are established to filter standards that best meet the needs of SWITHs. Through this process, we identify two key standards that present themselves as the most promising alternatives. Moreover, we delineate the technical constraints involved in selecting the maximum nominal working pressure for high-pressure tanks intended for burst tests. Our findings reveal a current upper limit of 525 bar, imposed by the limitations of existing test facilities. The study underscores that these standards should serve as flexible guiding frameworks rather than rigid protocols, encouraging progressive development in the SWITH field.

The G protein-coupled receptor (GPCR) Smoothened (Smo) is a central signal transducer of the Hedgehog (Hh) pathway which has been linked to diverse forms of tumours. Stimulated by advancements in structural and functional characterisation, the Smo receptor has been recognised as an important therapeutic target in Hh-driven cancers, and several Smo inhibitors have now been approved for cancer therapy. This receptor is also known to be an oncoprotein itself and its gain-of-function variants have been associated with skin, brain, and liver cancers. According to the COSMIC database, oncogenic mutations of Smo have been identified in various other tumours, although their oncogenic effect remains unknown in these tissues. Drug resistance is a common challenge in cancer therapies targeting Smo, and data analysis shows that healthy individuals also harbour resistance mutations. Based on the importance of Smo in cancer progression and the high incidence of resistance towards Smo inhibitors, this review suggests that detection of Smo variants through tumour profiling could lead to increased precision and improved outcomes of anti-cancer treatments.

This work deals with a novel porous TiNi-based material for ophthalmic orbital implants that was produced by high-temperature synthesis (SHS) and possesses a specific microstructure of its surface pores, which makes it attractive for biomedical use in implants. After preparation via SHS, the obtained porous TiNi material was etched in acidic environment, to get rid of its surface Ti2Ni secondary-phase particles. This was found to improve the material’s surface morphology, adding micro-roughness to its macro-rough pores. As a result, cell growth tests conducted on the material demonstrated improved cell adhesion and growth kinetics on such a porous material with improved roughness. Finally, the material was tested in vivo as an ophthalmic orbital implant, demonstrating good biocompatibility, good degree of biointegration with surrounding eyeball tissues, and no signs of rejection after as long as 180 days. Thus, the novel porous TiNi-based material shows promise for its use in ophthalmic implantology, for instanse for manufacturing musculoskeletal stumps of the eyeball after evisceration, as it is biocompatible, has a high tissue-implant integration potential and demonstrates reduced risks of exposure and rejection of the implant.

Precision cancer medicine primarily aims to identify individual patient genomic variations and exploit vulnerabilities in cancer cells to select suitable patients for specific drugs. These genomic features are commonly determined by gene sequencing prior to therapy, to identify individuals who would be most responsive. This precision approach in cancer therapeutics remains a powerful tool that benefits a smaller pool of patients, sparing others from unnecessary treatments. A limitation of this approach is that proteins, not genes, are the ultimate effectors of biological functions, and therefore the targets of therapeutics. An additional dimension in precision medicine that considers an individual’s cytokine response to cancer therapeutics is proposed. Cytokine responses to therapy are multifactorial and vary among individuals. Thus, precision is dictated by the nature and magnitude of cytokine responses in the tumor microenvironment exposed to therapy. This review highlights cytokine responses as modules for precision medicine in cancer therapy, including potential challenges. For solid tumors, both detectability of cytokines in tissue fluids and their being amenable to routine sensitive analyses could address the difficulty of specimen collection for diagnosis and monitoring. Therefore, in precision cancer medicine, cytokines offer rational targets that can be utilized to enhance the efficacy of cancer therapy.

We hypothesized that reticuloruminal temperature and pH, as well as cow activity, can be used as biomarkers for the early diagnosis of clinical mastitis in dairy cows. Therefore, we aimed to detect the relationship between reticuloruminal temperature and pH, cow activity, and clinical mastitis in dairy cows. We randomly selected cows with clinical mastitis and clinical healthy cows (HG) out of 600 milking cows. We recorded the following parameters during the experiment: reticulorumen tem-perature (RR temp.), reticulorumen pH (RR pH), and cow activity. We used smaXtec boluses (smaXtec animal care technology®, Graz, Austria). In this investigation, reticulorumen data ob-tained seven days before diagnosis were compared to HG data from the same time period. CM cows were observed on the same days as the healthy cows. The healthy group’s RR pH was 7.32% higher than that of cows with CM. Reticulorumen temperature was also 1.25% higher in the CM group than in the control group. The healthy group had a higher average value for walking activity and was 17.37% higher than the CM group. The data of reticulorumen pH changes during 24 h showed that during the day, the pH changed from 5.53 to 5.83 in the CM group. By contrast, pH changed from 6.05 to 6.31 in the control group. The lowest reticulorumen pH in the CM group was detected on the third day before diagnosis. It was 15.76% lower than the highest reticulorumen pH detected on the sixth day before diagnosis. The lowest reticulorumen pH in CM cows was detected at 0 and 1 days before diagnosis. It was 1.45% lower than the highest reticulorumen pH detected on the second day before diagnosis. The lowest walking activity in the CM group was detected 0 days before diagnosis, 50.60% lower than on the fifth day before diagnosis. The lowest walking activity was detected 0 days before diagnosis, 39.57% lower than on the seventh day before diagnosis. In this study, we found that reticuloruminal temperature, reticuloruminal pH, and cow ac-tivity could be used as biomarkers for the early diagnosis of clinical mastitis in dairy cows.

Here, we report the prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight RGB image vegetation indices for the canary bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations.

Site Index has been widely used as an age normalised metric to account for variation in forest height at a range of spatial scales. Although previous research has used a range of modelling methods to describe regional variation in Site Index little research has examined gains that can be achieved through use of regression kriging or spatial ensemble methods. In this study an extensive set of environmental surfaces were used as covariates to predict Site Index measurements covering the environmental range of \textit{Pinus radiata} D. Don plantations in Chile. Using this dataset, the objectives of this research were to (i) compare predictive precision of a range of geostatistical, parametric and non-parametric models, (ii) determine if significant gains in precision can be attained through use of regression kriging, (iii) evaluate the precision of a spatial ensemble model that utilises predictions from the five most precise models, through using the model prediction with lowest error for a given pixel and (iv) produce a map of Site Index across the study area. The five most precise models were all geostatistical and included ordinary kriging and four regression kriging models that were based on partial least squares or random forests. A spatial ensemble model constructed from these five models was the most precise of those developed (RMSE = 1.851 m, RMSE% = 6.38%) and had relatively little bias. Climatic and edaphic variables were the strongest determinants of Site Index and in particular, variables related to soil water balance were well represented within the most precise predictive models. These results highlight the utility of predicting Site Index using a range of approaches, as these can be used to construct a spatial ensemble that may be more precise than predictions from the constituent models.

Tumour necrosis factor-α is a key mediator of inflammation in rheumatoid arthritis its discovery led to the development of highly successful anti-TNF therapy. Subsequently, other biologic drugs targeting immune pathways, namely interleukin-6 blockade, B cell depletion, and T cell co-stimulation blockade, have been developed. Not all patients respond to a biologic drug leading to a knowledge gap between biologic therapies available and the confident prediction of response. So far, genetic studies have failed to uncover clinically informative biomarkers to predict response. Given that the targets of biologics are immune pathways, immunological study has become all the more pertinent. Furthermore, advances in single cell technology have enabled the characterisation of many leucocyte subsets. Studying the blood immunophenotype may therefore define biomarker profiles relevant to each individual patient’s disease and treatment outcome. This review summarises our current understanding of how immune biomarkers might be able to predict treatment response to biologic drugs.

To improve the understanding of soil–pile interaction of laterally loaded piles, the quasi-static test of pile group foundation in silty clay with 5 foundation piles arranged in cross shape is carried out. The direct measurement-based p–y curves at different depths showed nonlinear behavior, in which the initial stiffness and ultimate soil reaction increased as the depth increased. The loading displacement amplitude corresponding to the failure of each foundation pile in the high axial compression reinforced concrete pile group foundation is given as follows: the front-end nappe foundation pile (#3 pile) is 6mm, the middle pile is 16mm, the front-end nappe foundation pile is 20mm, and the loading displacement amplitude of side pile is 24mm. In silty clay, due to the existence of pile group effect, the edge effect has little effect on the horizontal foundation bed coefficient of foundation pile in pile group, while the influence of masking effect on the horizontal foundation bed coefficient of each foundation pile in pile group is not related to the position of pile, but only to the existence of masking effect, and the influence of different columns of masking effect on the horizontal foundation bed coefficient is the same.

Multi-junction solar cells comprised of stacked III-V semiconductor junctions represent the highest-efficiency photovoltaic technology, with recent demonstrations exceeding 47% efficiency . Optimizing the design and thickness of each junction is critical for maximizing performance . This work utilizes Silvaco TCAD tools to systematically optimize a 5-junction cell based on AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge similar to recent record cells . The junction thicknesses are varied using a predictive profiler to sample the parameter space . For each combination, the spectral absorption and I-V characteristics are simulated to determine the efficiency . Statistical analysis identifies the optimal thickness set that maximizes performance to 26% under 1 sun illumination .

Sample size using prevalence has been one of the most important medical research methodologies and has been used in original articles more often than any other statistical tool available. The simple formula consists of variables interdependent and it is as important to understand how they influence each other as it is to learn to use the formula to calculate the sample size. With the choice of different amounts of precision and levels of confidence available to a medical researcher, it should be of paramount importance to understand what a slight change in choosing the parameter would mean at the end of the research. Understanding of different Z values, Levels of precision, and ranging Prevalence is required to understand the effect on the sample size and subsequently on the research. Topological representation using parameter ranges can be used to demonstrate this relationship.

Vaccination represents the historical milestone in medical sciences, which has been saving millions of lives. Nonetheless, continued outbreaks of emergent infectious diseases are swamping from every coast to every border all over the world. Modern vaccines are our most powerful arsenals to defend the public health of a global population ever since 1796. As unexpected, modern vaccination approaches have brought out all sorts of unwelcome pathogenic disorders to varying extents. Pathogenic viruses have also continued to evolve to defeat elaborate arsenals devised by human intelligence. In this review, we have covered a variety of vaccination approaches along with their sophisticated materials from the past to the most recent development. Furthermore, we discuss varied serious pathogenic outcomes elicited by contemporary vaccination programmes involving novel materials in real clinical trials. The complex immune mediators will be discussed in the context to tackle the complication of vaccine-induced pathogenesis. We will offer the insight into the intractable discrepancy of vaccine-associated pathology. The precision approach bestowed with evolutionary concepts will be considered as a feasible option to confront various issues of pathogenic complicacy.

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest to incorporate technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, South of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture, pH, organic matter -OM, phosphorous -P2O5, potassium -K2O, sum of the exchange bases -SEB; and cation exchange capacity -CEC) which were determined from 20 soil samples taken in the different MZs. Additionally, validation was also performed using Rasch measures, which were defined based on the formula-tion of the objective and probabilistic Rasch model, integrating the information from the afore-mentioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. The use of ECa constitutes an efficient technological tool for implementing site-specific man-agement in viticulture, which allows for the improvement of decision-making processes by con-sidering the inherent spatial variability of the soil.

This study examines the impact of alcohol concentration on the precision of unmanned aerial vehicle (UAV) operations. While the impact of alcohol impairment on various activities is well documented, its specific influence on drone piloting accuracy remains less studied. To address this, we carried out empirical research using an original methodology and a real-world environment. The study involved a group of 35 participants performing a series of flights using a selected drone (DJI Mavic2 Pro) on a bespoke obstacle route. Between each flight series, participants consumed a measured amount of alcohol, designed to incrementally increase their blood alcohol concentration monitored via breath tests. The precision of the drone pilot was evaluated by comparing various parameters from successive test flights, such as time, distance, average speed, and incident frequency, against a baseline trial executed with zero alcohol concentration. Our results indicate a noticeable decline in UAV piloting precision corresponding with increasing alcohol concentration. Parameters such as flight time, distance, speed, and incident frequency all demonstrated this trend. We conclude that alcohol consumption detrimentally affects the accuracy of drone operations. These findings underscore the need for clear policies and guidelines regarding the operation of UAVs under the influence of alcohol.

Asthma is a prevalent disease that around 300 million worldwide, resulting in substantial morbidity, mortality, and economic burden on a global scale. New clinical and laboratory research has shed light on the immunology causing asthma. Asthma is now recognized as a heterogeneous disease. A personalized medicine is based on the classification of asthma by endotype by linking observable characteristics to immunological mechanisms. Identifying endotype mechanisms is essential for better characterizing patients and personalizing therapeutic approaches with novel biological agents that target specific immune pathways. This article provides a summary of the major immunological mechanisms involved in the pathogenesis and emergence of the disease's phenotypic features, as well as the individualized treatment for severe asthma subtypes

Primary Biliary Cholangitis (PBC) is a chronic, cholestatic liver disease with a heterogeneous presentation, symptomatology, disease progression, and response to therapy. The current risk stratification assessment, aimed at identifying patients with a higher risk of disease progression, encompasses an in-depth analysis of demographic data, clinical and laboratory findings, antibody profiles, and the evaluation of liver fibrosis using both invasive and non-invasive techniques. Treatment response scores after one year of therapy remain to date a major factor influencing the prognosis of PBC patients. While the initial therapeutic approach with ursodeoxycholic acid (UDCA) is universally applied, new second-line treatment options have recently emerged, with many others under investigation. Consequently, the prevailing one-size-fits-all approach is poised to be supplanted by tailored strategies, ensuring high-risk patients receive the most appropriate treatment regimen from diagnosis. This will require the development of a risk prediction model to assess, at the time of diagnosis, the course, outcome, and response to first and additional treatments of PBC patients. This manuscript provides a comprehensive overview of the current and emerging tools used for risk stratification in PBC and speculates on how these developments might shape the disease landscape in the near future.

Profitability is an underestimated concept in precision agriculture. In this research, a new module is developed within a pre-existing farm management system to assess the profitability of precision agriculture applications in extended crops. The module is regulated on a 5-meter spatial resolution, thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale. A bottom-up approach, taking advantage of the full functionality of the farm management system, together with a flexible architecture and an easy-to-use interface, renders the new module an innovative commercial application.

Diabetes and diabetes-induced micro and macrovascular complications are major causes of healthcare problems. The extracellular vesicles are produced by all cells from all organisms. They are shown to have properties of the producer cells and also reflect the host physiology and pathology. Since the EVs represent the cellular state they are excellent biomarkers. In this review, we will focus on the EVs produced by the Neuro Vascular Unit cells of the eye as well as the extraocular cells in the onset and progression of diabetic retinopathy. A brief account of the use of these nanovesicles in personalised medicine is also given.

A strategy to reduce qualitative and quantitative losses in crop-yields refers to early and accurate detection of insect-damage caused in plants. Remote sensing systems like hyperspectral proximal sensors are a promising strategy for managing crops. In this aspect, machine learning predictions associated with clustering techniques may be an interesting approach mainly because of its robustness to evaluate high dimensional data. In this paper, we model the spectral response of insect-herbivory-damage in maize plants and propose an approach based on machine learning and a clustering method to predict whether the plant is herbivore-attacked or not using leaf reflectance measurements. We differentiate insect-type damage based on the spectral response and indicate the most contributive wavelengths to perform it. For this, we used a maize experiment in semi-field conditions. The maize plants were submitted to three different treatments: control (health plants); plants submitted to Spodoptera frugiperda herbivory-damage, and; plants submitted to Dichelops melacanthus herbivory-damage. The leaf spectral response of all plants (controlled and submitted to herbivory) was measured with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We evaluated the performance of different learners like random forest (RF), support vector machine (SVM), extreme gradient boost (XGB), neural networks (MLP), and measured the impact of a day-by-day analysis into the prediction. We proposed a novel framework with a ranking strategy, based on the accuracy returned by predictions, and a clusterization method based on a self-organizing map (SOM) to identify important regions in the reflectance measurement. Our results indicated that the RF-based framework algorithm is the overall best learner to deal with this type of data. After the 5th day of analysis, the accuracy of the algorithm improved substantially. It separated the three treatments into different groups with an F-measure equal to 0.967, 0.917, and 0.881, respectively. We also verified that the most contributive spectral regions are situated in the near-infrared domain. We conclude that the proposed approach with machine learning methods is adequate to monitor herbivory-damage of S. frugiperda and stink bugs like Dichelops melacanthus in maize, differentiating the types of insect-attack early on. We also demonstrate that the framework proposed for the analysis of the most contributive wavelengths is suitable to highlight spectral regions of interest.

Accurate expression of bracket prescription is important for successful orthodontic treatment. The aim of this study was to evaluate the accuracy of digital scan images of brackets produced by four different intraoral scanners (IOSs) in terms of the height, position, and angle of the bracket slot when scanning the surface of dental model attached with bracket materials made from different composition of materials. Brackets made from stainless steel, polycrystalline alumina, composite and composite/stainless steel slot were considered, which have been scanned from 4 different IOSs (Primescan, Trios, CS3600 and i500). SEM images were used as references. Each bracket axis was set in the reference scan image, and the axis was set identically by superimposing with the IOS image, and then only the brackets were divided and analyzed. The difference between the manufacturer's nominal torque and bracket slot base angle was 0.39 in SEM, 1.96 in Primescan, 2.04 in Trios, and 5.21 in CS3600 (P <0.001). The parallelism, which is the difference between the upper and lower angles of the slot wall, was 0.55 in SEM, 7.55 in Primescan, 6.74 in Trios3, 6.59 in CS3600, and 24.95 in i500 (p <0.001). This study evaluated the accuracy of the bracket only and it must be admitted that there is some error in recognizing slots through scanning in general

This work introduces a device for long term systematic monitoring of trees against borers. A widely applied way to detect wood-boring insects is to insert a piezoelectric probe with uncoated waveguide in the tree trunk and listen for locomotion or feeding sounds through headphones. This approach has several shortcomings: a) frequent manual inspection of trees is costly and impractical to be scaled to hundreds or thousands of trees, b) the larvae could be present but inactive during the inspection time and, c) when the trees are in urban environments the background noise can be significant and can mask the feeble sounds of wood-boring insects even with the use of specialized headphones. We introduce a remotely controlled device that records and wirelessly transmits on a scheduled basis short recordings of the internal vibrations of a tree to a server. The user can listen remotely or process the recording automatically to infer the infestation state of the tree with wood-boring insects that feed or move inside the tree. When integrated within the IoT framework this device can scale up to automatically monitoring the trees of the entire city. The proposed approach led to detection results in field trials of the pests Xylotrechus chinensis and Rhynchophorus ferrugineus.

The primary measure of the quality of sea surface temperature (SST) fields obtained from satellite-borne infrared sensors has been the bias and variance of matchups with co-located in-situ values. Because such matchups tend to be widely separated, these bias and variance estimates are not necessarily a good measure of small scale (several pixels) gradients in these fields because one of the primary contributors to the uncertainty in satellite retrievals is atmospheric contamination, which tends to have large spatial scales compared with the pixel separation of infrared sensors. Hence, there is not a good measure to use in selecting SST fields appropriate for the study of submesoscale processes and, in particular, of processes associated with near-surface fronts, both of which have recently seen a rapid increase in interest. In this study, two methods are examined to address this problem, one based on spectra of the SST data and the other on their variograms. To evaluate the methods, instrument noise was estimated in Level-2 VIIRS and AVHRR SST fields of the Sargasso Sea. The two methods provided very nearly identical results for AVHRR: along-scan values of approximately 0.18 K for both day and night and along-track values of 0.21 K also for day and night. By contrast, the instrument noise estimated for VIIRS varied by method, scan geometry and day-night. Specifically, daytime, along-scan (along-track), spectral estimates were found to be approximately 0.05 K (0.08 K) and the corresponding nighttime values of 0.02 K (0.03 K). Daytime estimates based on the variogram were found to be 0.08 K (0.10 K) with the corresponding nighttime values of 0.04 K (0.06 K). Taken together: AVHRR instrument noise is significantly larger than VIIRS instrument noise, along-track noise is larger than along-scan noise and daytime levels are higher than nighttime levels. Given the similarity of results and the less stringent preprocessing requirements, the variogram is the preferred method although there is a suggestion that this approach overestimates the noise for high quality data in dynamically quiet regions. Finally, simulations of the impact of noise on the determination of SST gradients show that on average the gradient magnitude for typical ocean gradients will be accurately estimated with VIIRS but substantially overestimated with AVHRR.

This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

Over the last two decades, a considerable amount of equipment has been acquired (spreaders, seeders, sprayers, among others) to respond to the challenges of Precision Agriculture (PA) concept. Most of this equipment has been purchased at a high cost. However, many of them, despite still being functional and equipped with sensors, actuators, and electronic processing units capable of adjusting to variations in speed, have become obsolete in terms of communication, and incompatible with new monitoring and control systems based on the “Isobus” protocol. This work aims to present a solution for updating the control system (“Ferticontrol”) of a “Vicon RS-EDW” spreader with variable rate application (VRA), making it compatible with the “InCommand” system from “Ag Leader”. The solution includes low-cost “Arduino” and “Raspberry Pi” microcontrollers and open-source software. The development shows that it is possible to implement a solution that is accessible to farmers in general. It also provides a niche business opportunity for young researchers to set up small technology-based enterprises associated with universities and research centers. These partnerships guarantee permanent innovation and represent a decisive step towards modern, technological, competitive, and sustainable agriculture.

In this work, we explore new constraints on phantom scalar field cosmologies with a scalar field employing early times catalogues related to CMB measurements, along with the local standard observables, like Supernovae Type Ia (SNIa), H(z) measurements (Cosmic Clocks), and Baryon Acoustic Oscillations (BAO) baselines. In particular, we studied a tracker phantom field with hyperbolic polar coordinates that have been proposed in the literature. The main goal is to obtain precise cosmological constraints for H0 and σ8, in comparison to other constructions that present tension in early cosmological parameters. Our results show that phantom scalar field cosmologies have a reduced statistical tension on H0 that it is less than 3σ using model-independent CMB catalogues as SPT-3G+WMAP9 and ACTPol DR-4+WMAP9 baselines. This suggests these models using a different phantom potential might address the Hubble constant problem and reduce the systematics involved.

Significant advances in Penning trap measurements of atomic masses and mass ratios of the proton, deuteron, triton, helion, and alpha-particle have occurred in the last five years. These include a measurement of the mass of the deuteron against 12C with 8.5 x 10-12 fractional uncertainty; resolution of vibrational levels of H2+ as mass and the application of a simultaneous measurement technique to the H2+/D+ cyclotron frequency ratio, yielding a deuteron-proton mass ratio at 5 x 10-12; new measurements of HD+/3He+, HD+/T+ and T+/3He+ leading to a tritium beta-decay Q-value with an uncertainty of 22 meV, and atomic masses of the helion and triton at 13 x 10-12; and a new measurement of the mass of the alpha-particle against 12C at 12 x 10-12. Some of these results are in strong disagreement with previous literature values. Their impact in determining a precise proton/electron mass ratio and electron atomic mass from spectroscopy of the HD+ molecular ion is also discussed.

Fibroblast growth factor receptors (FGFRs) are a family of receptor tyrosine kinases that involve in regulation of cell proliferation, survival, and development. FGFR alterations including amplifications, fusions, rearrangements, and mutations can result downstream activation of tyrosine kinases leading to tumor development. Targeting those FGFR alterations has shown to be effective in cholangiocarcinoma, urothelial carcinoma, and myeloid/lymphoid neoplasms and there are currently four FGFR inhibitors approved by the Food and Drug Administration (FDA). There have been developments of multiple agents targeting FGFR pathway including selective FGFR inhibitors, ligand traps, monoclonal antibodies, and antibody drug conjugates. However, most of these agents have variable and low responses with some intolerable toxicities and acquired resistances. This review will summarize previous clinical experiences and current developments of agents targeting FGFR pathway and discuss future directions of FGFR targeting agents.

Radiomics refers to the extraction and analysis of a wide range of medical imaging features in a non-invasive and cost-effective manner to comprehensively characterise tumours. In this study, machine learning models combining radiomics and clinical factors were developed to predict retroperitoneal lymph node metastasis in testicular germ cell tumours (TGCTs), with the aim of reducing unnecessary treatment in this group of young patients. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. After segmenting 273 retroperitoneal lymph nodes using dedicated radiomics software, we developed machine-learning prediction models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). For each classifier, we developed a radiomics-only, clinical-only, and combined radiomics-clinical prediction model. The models’ performances were evaluated using the area under the receiver operating characteristic curves (AUCs). The RF-based combined clinical and radiomic model showed the most robust performance in predicting LNM with an area under the curve (AUC) of 0.95 (±0.03; 95% CI), accuracy 87%, precision 89%, recall 86% and F1 score 87%, followed by the LGBM model with an area under the curve (AUC) of 0.93 (±0.05; 95% CI), accuracy 83%, precision 87%, recall 80% and F1 score 82%. Decision curve analysis demonstrated the clinical utility of our proposed RF-based combined clinical–radiomics model. Our study has identified reliable and predictive machine-learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine-learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment. Multi-centre validation is required to provide high-quality evidence for clinical application.

The fight against suicide is highly challenging as it may be one of the most complex and at the same time most threatening among all psychiatric phenomena. In spite of its huge impact, and despite advances in neurobiology research, understanding and predicting suicide remains a major challenge for both researchers and clinicians. To be able to identify those patients who are likely to engage in suicidal behaviors and identify suicide risk in a reliable and timely manner, we need more specific, novel biological and genetic markers/indicators to develop better screening and diagnostic methods, and in the next step to utilize these molecules as intervention targets. One such potential novel approach is offered by our increasing understanding of the involvement of neuroinflammation based on multiple observations of increased proinflammatory states underlying various psychiatric disorders including suicidal behavior. The present paper overviews our existing understanding of the association between suicide and inflammation including peripheral and central biomarkers, genetic and genomic markers, and our current knowledge of intervention in suicide risk using treatments influencing inflammation, also overviewing the next steps to be taken and obstacles to be overcome before we can utilize cytokines in the treatment of suicidal behavior.

Aluminum alloy (Al6061) sheet micro-hole processing is extensively used in smartphones, tablet PC, and smart wearable devices. The micro-hole processing is commonly performed using laser, micro drilling, microstamping, micro discharge, and chemical etching technologies. Micro-stamping technology is characterized by precision and rapid processing, but the precision positioning of the punch head and lower die is one of the major difficulties in micro-hole stamping, especially in manufacturing array micro-holes. This study used stamping without die technology. This technology uses an array punch head to punch the lower die holes on the base, then performs array micro-hole stamping. The experimental results show that a micro-hole array with 37 micro-holes were successfully manufactured and can be reproduced many times. Tapered array micro-holes with a high aspect ratio can be manufactured using this stamping without die technology; the micro-hole depth can be 260μm, the inlet diameter is 116μm, and the outlet diameter is 25μm. This study has successfully developed the feasibility of array micro-hole stamping technology.

Agricultural production needs technologies that assist the management of natural resources, for example, the collection of real-time data on soil, water, weather, crops, and biodiversity conditions. Sensor technology solutions and open-source software are appropriate for promoting more sustainable agricultural production. Among the advantages of using open-source technologies and software is its potential for extension, collaboration, customization, flexibility, maintenance cost, transparency, speed, and better security. Given the above, the objective of this research was to find, in different electronic databases, exclusively open-source software for precision agriculture, offering a systematic review, and addressing considerations and challenges. This survey considers up-to-date open-source software available in repositories such as GitHub and GitLab, to understand its characteristics and application formats.

The aim of the current study was to determine the relation between subclinical mastitis and rumination behavior registered with an online sensor system. According to the results of the general clinical examination of 650 milking cows, 10 cows with subclinical mastitis (SCM) and 10 clinical healthy cows (HG) were selected (without clinical signs of any diseases). Rumination behavior biomarkers were registered with RumiWatch noseband sensor (RWS; ITIN + HOCH GmbH, Fütterungstechnik, Liestal, Switzerland). Sensors were implanted on the first day after calving. Data from the RWS from 13th days before the diagnosis and 13 days after diagnosis of SCM were used to compare with HG data during the same period. The healthy cows were monitored during the same days as the SCM cows.

Abstract: Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru.

The use of biomarkers to measure immune responses in serum is crucial for understanding population-level exposure and susceptibility to human pathogens. Advances in sample collection, multiplex testing, and computational modeling are transforming serosurveillance into a powerful tool for public health program design and response to infectious threats. In July 2018, 70 scientists from 20 countries met to perform a landscape analysis of approaches that support an integrated serosurveillance platform, including the consideration of issues for successful implementation. Here, we summarize the group’s insights and proposed roadmap for implementation, including objectives, technical requirements, ethical issues, logistical considerations, and monitoring and evaluation.

BACKGROUND: Glioblastoma (GBM) is driven by various genomic alterations. Next generation sequencing (NGS) could yield targetable alterations that may impact outcomes. The goal of this study was to describe how NGS can inform targeted therapy (TT) in this patient population. METHODS: The medical records of patients (pts) with a diagnosis of GBM from 2017-2019 were reviewed. Records of patients with recurrent GBM and genomic alterations were evaluated. Objective response rates and disease control rates were deter-mined. RESULTS: A total of 87 pts with GBM underwent NGS. Forty percent (n = 35) were considered to have actionable alterations. Of the 35, 40% (n=14) pts had their treatment changed due to an alteration. The objective response rate (ORR) of this population was 43%. The disease control rate (DCR) was 100%. The absolute mean decrease in contrast enhancing disease was 50.7% (95% CI 34.8 – 66.6). CONCLUSION: NGS for GBM, particularly in the recurrent setting, yields a high rate of actionable alterations. We observed a high ORR and DCR, reflecting the value of NGS in deciding on TT to match alterations that are likely to respond. In conclusion, patient selection and availability of NGS may impact outcomes in select pts with recurrent GBM.

Concomitant neuropathological hallmarks of Alzheimer’s Disease (AD) are common in the brains of people with Parkinson’s disease (PD). Furthermore, AD biomarkers are associated with cognitive decline and dementia in PD patients during life. Here, we highlight the considerable overlap between AD and PD, emphasizing neuropathological, biomarker, and mechanistic studies. We suggest that precision medicine approaches may successfully identify PD patients most likely to develop concomitant AD. The ability to identify PD patients at high risk for future concomitant AD in turn provides an ideal cohort for trials of AD-directed therapies in PD patients, aimed at delaying or preventing cognitive symptoms.

Colorectal cancer prognosis get worse with advancement of disease into metastatic stage. There is a pertinent need to develop prognostic biomarkers that can be used for personalized and precision medicine. Alternative splicing provides an insight into understanding of changes at isoform expression level which may not be evident at gene level. In this direction, we utilized our prior knowledge about significant alternatively spliced genes and chose ADAM12 and MUC4 for further characterization in a metastatic cell line model. These genes were found to be good prognostic indicators in The Cancer Genome Atlas database. We studied the gene organization and designed primers to specifically amplify a group of isoforms. Differential expression of these group of isoforms was observed in normal, primary and metastatic colorectal cancer cell lines. We further validated the results using sanger sequencing. Isoform expression was found to respond to the 5-fluorouracil treatment. RNAseq analysis of the cell lines further validated the differential expression of gene isoforms. Successful detection of ADAM12 and MUC4 in cell lysates varied according to the antibody used which may reflect differential expression of isoforms. This comprehensive study underscores the importance of studying alternatively spliced isoforms and their probable used as prognostic or predictive biomarkers.

The rising world population has made it imperative to get rid of time-consuming and non-economical agricultural practices. Thus, advances in Internet of Things (IoT) technology have recently propelled significant advancements in agriculture, similar to several other sectors. Through the combination of IoT and Farm Management Information Systems (FMIS), field data can be automatically collected, stored, analysed and accessed by farmers in real-time. In this paper, we present Proof-of-Concept (PoC) IoT circuit and FMIS web app for precision agriculture. The IoT circuit incorporates sensors, microcontroller and Wi-Fi module, which acquire and transfer field data to the FMIS web app. We designed and simulated the IoT circuit using Proteus, Arduino Uno, and Arduino Integrated Development Environment (IDE). The FMIS PoC web app was modelled with relevant Unified Modeling Language (UML) diagrams and Django (a Python framework) was employed to implement the app. Our simulation result illustrates how essentila field parameters can be monitored remotely and seamlessly by agriculture practitioners. It further provides a blueprint for real-time precision agriculture at scale and could serve as a learning aid for students in engineering and agricultural science.

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using UAV-acquired imagery. However, with the increased availability of UAVs with onboard GNSS RTK, georeferencing without GCPs is a promising alternative. However, systematic elevation error remains a problem of this technique. We aimed to analyze the reasons for this systematic error and propose strategies for the elimination of this error. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100m with vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° a 15°). Each of these flights was performed twice to produce a full double grid. Models were calculated from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights from the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS-RTK data and the deviation in the determined internal orientation parameters (focal length). Besides, we have shown that a combination of two flights with different image acquisition axis can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.

A 12 year old boy was diagnosed with temporal lobe epilepsy (TLE) along with mesial temporal sclerosis based on MRI (magnetic resonance imaging) results. The diagnosis was further confirmed by genetic analysis. He also had minor psychiatric symptoms of obsessive-compulsive disorders and mood swings. After 4 years of treatment with Sodium Valproate no change in symptoms was observed. Genetic testing along with deep phenotyping revealed altered glutamate pathway and metabolism. Post genetic testing the patient was put on a combination of Sodium Valproate and Valproic acid along with supplementation of N-acetylcysteine (NAC), Cyanocobalamin, Pyridoxine and Cholecalciferol. Within three months of this combined therapy the patient experienced complete elimination of seizures and drastic improvement in mood and social behaviour. The case report highlights the importance of precision diagnosis in understanding the underlying perturbed pathways in structural epilepsy like TLE and demonstrates the importance of non-invasive targeted therapy in such cases.

All over the world, development of micro blog and other social platform indicate that Social Media is now the focus and trend of the Internet. Daily life, study and work are influenced by news in Social Medias . Micro blog is new emergent type of media and it spreads information rapidly in the crowd in recent years. Suppose an user searches for specific information about one topic on micro blog. He/she found easily plenty of information related to his/her search in social medias. The problem is to find out the correct information. Normally, multi-document summarization method deals with a collection of documents about one topic for extracting the valuable points and discards useless information. Actually, it needs to extract the topic content by adding topic factors and social patterns. Topic factor is the lexical information related to the topic. Social pattern relates to special interactive mode owned by online social network, such as comment and repost. People has been seen the fake news on mobile/internet during lockdown period. It is of no doubt that anyone with a social media account has seen at least one example of this.Humanity’s greatest challenges are to detect false information. Fake news are collected from 150 persons using social media The aim of the paper is to investigate the truthfulness of the news people share on social media using K-nearest Neighbour (KNN) based Classifier method.

Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct tree branches from laser scans. We first extract an initial tree skeleton from the input tree point cloud, then simplify the skeleton through iteratively removing redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.

In response to the National Institutes of Health (NIH) All of Us Medicine Research Initiative, the Precision Medicine Research (PreMeR) Diversity Consortium was formed by four institutions from the Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN). This synergistic approach proposed evidence-based, best practices used by experienced researchers to engage, recruit and retain diverse populations in the All of Us initiative. Conceptualization of the proposed approach was aided by social influence theories to better understand how people’s beliefs and opinions should be modified to affect change leading to action [1]. The Social-Ecological Model (SEM), for Health Promotion [2], from Stokols [1] and Community-Based Participatory (CBPR) Models[3],[4] guided proposed engagement, recruitment, and retention strategies contextualized with the individual, interpersonal, organizational, community, and policy spheres of influence. The PreMeR produced a partnership to evaluate and improve the effectiveness of current engagement, recruitment, and retention strategies for minority participation in scientific studies. This approach illustrates the need to incorporate multiple methods of engagement to reach a diverse audience to participate in scientific research. Engagement, recruitment, and retention strategies in community and biomedical research must be viewed as community engaged public health interventions, utilizing the same theoretical principles and approaches.

Τhe concept of remote insect surveillance at large spatial scales for a number of serious insect pests of agricultural and medical importance is introduced in a series of our papers. We augment typical, low-cost plastic traps for many insect pests with the necessary optoelectronic sensors to guard the entrance of the trap in order to detect, time-stamp, GPS tag, and –in relevant cases- identify the species of the incoming insect from their wingbeat. For every important crop pest there are monitoring protocols to be followed in order to decide when to initiate a treatment procedure before a serious infestation occurs. Monitoring protocols are mainly based on specifically designed insect traps. Traditional insect monitoring suffers in that the scope of such monitoring: is curtailed by its cost, requires intensive labor, is time consuming, an expert is often needed for sufficient accuracy and can sometimes raise safety issues for humans. These disadvantages reduce the extent to which manual insect monitoring is applied and therefore its accuracy, which finally results in significant crop loss due to damage caused by pests. With the term ‘surveillance’ we intend to push the monitoring idea to unprecedented levels of information extraction regarding the presence, time-stamping detection events, species identification and population density of targeted insect pests. Insect counts as well as environmental parameters that correlate with insect’s population development are wirelessly transmitted to the central monitoring agency in real time, are visualized and streamed to statistical methods to assist enforcement of security control to insect pests. In this work we emphasize on how the traps can be self-organized in networks that collectively report data at local, regional, country, continental, and global scales using the emerging technology of the Internet of Things (IoT). This research is necessarily interdisciplinary and falls at the intersection of entomology, optoelectronic engineering, data-science and crop science and encompasses the design and implementation of low-cost, low-power technology to help reduce the extent of quantitative and qualitative crop losses by many the most significant agricultural pests. We argue that smart traps communicating through IoT to report in real-time the level of the pest population from the field straight to a human controlled agency can, in the very near future, have a profound impact on the decision making process in crop protection and will be disruptive of existing manual practices. In the present study, three cases are investigated : monitoring Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) using a) Picusan and b) Lindgren trap, and c) monitoring various stored grain beetle pests using the pitfall trap.

Generalized relativistic pseudopotentials (GRPP) of atomic cores implying the use of different potentials for atomic electronic shells with different principal quantum numbers give rise to accurate and reliable relativistic electronic structure models of atoms, molecules, clusters, and solids. These models readily incorporate the effects of Breit electron-electron interactions and one-loop quantum electrodynamics effects. Here we report the computational procedure for evaluating one-electron integrals of GRPP over contracted Gaussian functions. This procedure was implemented in a library of routines named LIBGRPP, which can be integrated into existing quantum chemistry software, thus enabling the application of various methods to solve the many-electron problem with GRPPs. Pilot applications to electronic transitions in the ThO and UO2 molecules using the new library and intermediate-Hamiltonian Fock space relativistic coupled cluster method are presented. The results clearly demonstrate that rather economical tiny-core GRPP models can exceed in accuracy relativistic all-electron models defined by Dirac-Coulomb and Dirac-Coulomb-Gaunt Hamiltonians.

Biliary tract cancers, including intrahepatic, perihilar, and distal cholangiocarcinomas, as well as gallbladder cancer, are a heterogeneous group of cancers with distinct molecular characteristics in each anatomic and pathological subtype. The pathological classification of BTC includes grossly classifiable growth patterns, such as mass-forming, periductal infiltrating, and intraductal growing. The small duct and large duct types of intrahepatic cholangiocarcinoma have been recently introduced in the WHO classification. Typical clinical presentations and various radiological, endoscopic, and molecular modalities for diagnosis are described in detail. To overcome the limitations of traditional tissue acquisition, new diagnostic modalities are being investigated. In fact, the treatment landscape is evolving rapidly owing to the emergence of distinct subgroups harboring unique molecular alterations with corresponding targeted therapies. In addition, we highlight the key points in the diagnostic process in real-world clinical practice for biliary tract cancer.

Myelodysplastic neoplasm (MDS) is a heterogenous clonal disorder of hemopoietic stem cells charac-terized by cytomorphologic dysplasia, ineffective hematopoiesis, peripheral cytopenias and risk of pro-gression to Acute Myeloid Leukemia (AML). Our understanding of this disease has continued to evolve over the last century. More recently prognostication and treatment are determined by cytogenetic and molecular data. Specific genetic abnormalities such as 5q deletion (del 5q), TP53 inactivation and SF3B1 mutation, are increasingly associated with disease phenotype and outcomes, as reflected in the recently updated 5th edition of the World Health Organization Classification of Hematolymphoid Tumors (WHO5) and International Consensus Classification 2022 (ICC 2022) classifications. Treatments are in-creasingly being tailored for patient specific phenotypes, and trials exploring the efficacy of targeted treatments are ongoing in this era of precision medicine.

Neurocutaneous disorders, also known as phakomatoses, are congenital and acquired syndromes resulting in simultaneous neurologic and cutaneous involvement. In several of these conditions, the genetic phenomenon is understood, providing a pivotal role in the development of therapeutic options. This review encompasses the discussion of the genetic and clinical involvement of neurocutaneous disorders and examines clinical management and treatment options. With the current advances in genetics, the role of precision medicine and targeted therapy plays a substantial role in addressing management of these conditions. The interconnectedness between therapeutic options highlights the importance of precision medicine in treating each disorder’s unique molecular pathway. This review provides an extensive synthesis of ongoing and current therapeutics in the management of such clinically unique and challenging conditions.

The current review comprehensively explores the multifaceted landscape of anaplastic lymphoma kinase (ALK) and its pivotal role, specifically within non-small cell lung cancer (NSCLC). It traces the trajectory of ALK's discovery, notably its fusion with nucleolar phosphoprotein (NPM)-1 in anaplastic large cell non-Hodgkin's lymphoma (ALCL) in 1994. It unfolds the subsequent impact of ALK gene alterations across various malignancies, including inflammatory myofibroblastoma (IMT) and NSCLC. The prevalence of intricate ALK rearrangements affecting roughly 3–5% of NSCLC patients prompted the approval of six ALK-tyrosine kinase inhibitors (TKIs) by 2022, revolutionizing the treatment landscape for advanced metastatic ALK+ NSCLC. Second-generation TKIs like alectinib, ceritinib, and brigatinib have notably emerged to combat resistance issues initially associated with crizotinib, the pioneer ALK-TKI approval. Moreover, this review delves into the intersection of ALK and immunotherapy, exploring the potential of immune checkpoint inhibitors in ALK-altered NSCLC tumors. Despite promising preclinical data, challenges encountered in trials evaluating combinations like nivolumab-crizotinib, primarily due to severe hepatic toxicity, underscore the need for cautious exploration of these novel approaches. Additionally, the review delves into innovative avenues such as ALK vaccines and biosensors, shedding light on their promising potential within ALK-driven cancers. This comprehensive analysis encompasses molecular mechanisms, therapeutic strategies, and immune interactions associated with ALK-rearranged NSCLC. Ultimately, the review is a pivotal resource guiding future research and therapeutic interventions in ALK-targeted therapy for NSCLC.

Precision agriculture including camera-guided row detection and hydraulic steering are often used as an alternative because crop damage can be decreased and driving speed can be increased comparable to herbicide applications.The effects of different approaches as uncontrolled (UC), mechanical weed control (MWC), herbicide weed control (HWC) and mechanical + herbicide control (MWC+HWC) on weed density and yield of sugar beet were tested and evaluated in two trials (2021 and 2022) in South Transylvania at the tested intervals BBCH 19 and 31. Weed control efficacy (WCE) depends on the emergence of the weeds and a good timing of weed controls in all the trials and methods, though the highest yield of sugar beet roots was recorded in the treatment MWC+HWC, with an increase up to 12-15% (56.48 t ha-1) yield from HWC (50.22 t ha-1) and a yield increase of more than 35- 40% than MWC (42.34 t ha-1). Our trials show that is possible to increase yield and have less chemical applications with the introduction of the new precision technologies in agriculture included here the sensor-guided mechanical controls.

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision-making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of data sets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Precision agriculture (PA) is a holistic, sustainable, innovative systems approach that assists farmers in production management. Adopting PA could improve sustainable food security and community economic sustainability. Developing an understanding of PA adoption attributes is needed to assist extension practitioners to promote adoption and better understand the innovation adoption phenomena. A systematic review of literature was conducted to investigate PA adoption. Thirty-three publications were examined, and four themes were found among the reviewed publications. The results were interpreted using Rogers’ diffusion of innovations framework to address the research objectives. Of the reviewed literature, we found relative advantage and compatibility were two dominant attributes to strengthen the adoption of PA, and the complexity attribute was rarely used to promote the adoption of PA. This study shows that change agents do not fully use five attributes of innovation when they promote PA technology to stakeholders to adopt. Thus, we recommend studies from the agricultural extension specialists’ perspectives in the future may determine contributions to motivate farmers’ adoption of PA, in particular related to complexity.