Patients with Parkinson’s Disease (PD) experience REM sleep behavior disorder (RBD) more frequently than healthy controls. RBD is associated with torpid disease evolution. To test the hypothesis that differential genetic signatures might contribute to the torpid disease evolution in PD patients with RBD. We compared the rate of genetic mutations in PD patients with or without probable RBD. Patients with a clinical diagnosis of PD in the Parkinson’s Progression Markers Initiative (PPMI) database entered the study. We excluded those with missing data or a diagnosis change over the first five years from the initial PD diagnosis. Probable RBD (pRBD) was confirmed by a REM Sleep Behavior Disorder Screening Questionnaire score >5 points. Logistic regression and Machine Learning (ML) algorithms were used to relate Single Nucleotide Polymorphism (SNPs) in PD-related genes with pRBD. We included 330 PD patients fulfilling all inclusion and exclusion criteria. The final logistic multivariate model revealed that the following SNPs increased the risk of pRBD: GBA_N370S_rs76763715 (OR, 95% CI: 3.38, 1.45-7.93), SNCA_A53T_rs104893877 (8.21, 2.26-36.34), ANK2. CAMK2D_rs78738012 (2.12, 1.08-4.10), and ZNF184_rs9468199 (1.89, 1.08-3.33). Conversely, SNP COQ7. SYT17_rs11343 reduced pRBD risk (0.36, 0.15-0.78). The ML algorithms led to similar results. The predictive models were highly specific (95-99%) but lacked sensitivity (9-39%). We found a distinctive genetic signature for pRBD in PD. The high specificity and low sensitivity of the predictive models suggest that genetic mutations are necessary but not sufficient to develop RBD in PD. Additional investigations are needed.

Since the outbreak of the COVID-19 pandemic, patients infected with COVID-19 may experience abnormal heart rates, posing potential health risks. This study proposes a non-contact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in more accurate diagnosis and treatment. In this study, except for the different image acquisition method, other research processes and methods are similar to the research methods in [15]. The image acquisition method is changed from forehead image to palm image. The new non-contact measurement performance of the proposed method can effectively not only avoid infection concerns but also obtain heart rate and HRV quickly and conveniently, providing higher accuracy for physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to those in recently published papers.

The objective of this study was to investigate successional changes in water flow as a result of changes in soil hydraulic properties after crop abandonment under drought and non-drought conditions, and under water uptake by co-occurring perennial plant species to clarify the observation that typical perennial grass species are seldom observed in abandoned fields. Soil hydraulic properties were measured in croplands which had been abandoned for different periods (2, 9, and 18 years from abandonment) and in a grazed grassland site. Hydrological processes in the soil profiles were simulated with soil hydraulic properties under drought and non-drought summer conditions with water uptake from perennial grass species Suction in the surface soils increased with the period of abandonment, with this trend being particularly obvious in a drought year. Available water appears to be restricted in the later successional stage of abandoned fields and in grazed grassland for plants that have drought tolerance. Dry soil and climate conditions are important factors determining the intrusion of the typical perennial grass, S. krylovii, into degraded abandoned fields. This abiotic interaction between soil hydraulic properties and climate conditions may play an important role for plant succession in abandoned cropland.

Building carbon footprint analysis is a key instrument for assessing the impact of different constructions on climate change. Several standards and methodologies are available to calculate the footprint of buildings, including standards and norms, life cycle assessment (LCA), and dedicated software tools. The use of BIM software for these calculations is both scientifically justified and very practical. This scientific publication focusses on the application of a BIM-based research methodology for the analysis of the carbon footprint of a single-family house. The research process included the following steps: (i) design of a single-family house with timber frame construction using Archicad BIM software, (ii) simulation of the building energy performance using the EcoDesigner Star plug-in, (iii) life cycle assessment (LCA) using the plug-in for Archicad, (iv) preparation of a second model with traditional masonry construction for comparison, and (v) comparative analysis of the single-family house models with timber frame and masonry construction. Analysis of the results highlights significant differences in CO2e emissions between masonry and timber buildings and the varying impact of individual elements on the total CO2e emissions of the buildings studied. These findings are relevant for future work on sustainable building design and construction, which aims to minimise negative environmental impacts. The goal of minimising the cumulative carbon footprint of buildings is critical to achieving the Sustainable Development Goals and combating climate change.

In this study, we propose an efficient field-free line (FFL) generator for mechanically driven FFL-magnetic particle imaging (MPI) applications. The novel FFL generator comprises pairs of Halbach arrays and bar magnets. The proposed design generates high-gradient FFLs with low-mass permanent magnets, realizing fine spatial resolutions in MPI. We investigate the generated magnetic field using simulations and experiments. Our results show that the FFL generator yields a high gradient of 4.76 T/m at a cylindrical field of view of 30 mm diameter and a 70 mm open bore. A spatial resolution of less than 3.5 mm was obtained in the mechanically driven FFL-MPI.

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) methods. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variation of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid this limitation inherent in the conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit to identify the global maximum power point (GMPP) even under fast fluctuation of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The novel adaptive PSO algorithm also opens new possibilities for further innovations in optimization-driven applications, extending its potential impact on a diverse range of real-world challenges. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority.

This paper presents research on the application of trajectory design, optimization, and control to an orbital transfer from Mars-Phobos Distant Retrograde Orbits to the surface of Phobos. Given a Distant Retrograde Orbits and a landing location on the surface of Phobos, landing trajectories for which total Δv for a direct 2-burn maneuver is minimized are computed. This is accomplished through the use of Particle Swarm Optimization in which the required Δv and time of flight are optimization parameters. The non-uniform gravitational environment of Phobos is considered in the computation. Results show how direct transfers can be achieved with Δv in the order of ∼30 m/s.

We derive the stabiliser group of the four-vector, also known as Wigner’s little group, in case of massless particle states, as the maximal solvable subgroup of the proper orthochronous Lorentz group of dimension four, known as the Borel subgroup. In the absence of mass, particle states are disentangled into left and right handed chiral states, governed by the maximal solvable subgroups sol2± of order two. Induced Lorentz transformations are constructed and applied to general representations of particle states. Finally, it is argued how the spin-flip contribution is closely related to the occurrence of nonphysical spin operators.

Radiotherapy using accelerated charged particles is rapidly growing worldwide. About 85% of the cancer patients receiving particle therapy is irradiated with protons, which have physical advantages compared to X-rays but similar biological response. In addition to the ballistic advantages, heavy ions present specific radiobiological features that can make them attractive for treating radioresistant, hypoxic tumors. An ideal heavy ion should have lower toxicity in the entrance channel (normal tissue), and being exquisitely effective in the target region (tumor). Carbon ions have been chosen because they represent the best combination in this direction. Normal tissue toxicities and second cancer risk are similar to those observed in conventional radiotherapy. In the target region, they have increased relative biological effectiveness and reduced oxygen enhancement ratio compared to X-rays. Some radiobiology properties of densely ionizing carbon ions are so distinct from X-rays and protons that they can be considered as a different “drug” in oncology, and may elicit favorable responses such as increased immune response and reduced angiogenesis and metastatic potential. The radiobiological properties of carbon ions should guide patient selection and treatment protocols to achieve optimal clinical results.

The availability of high quality surface observations of precipitation and volume observations by polarimetric operational radars make it possible to constrain, evaluate and validate numerical models with a wide variety of microphysical schemes. In this article, a novel particle-based Monte-Carlo microphysical model (called “McSnow”) is used to simulate the outer rain bands of Hurricane Dorian which traversed the densely instrumented precipitation research facility operated by NASA at Wallops Island, Virginia. The rain bands showed steady stratiform vertical profiles with radar signature of dendritic growth layers near −15 °C and peak reflectivity in the bright band of 55 dBZ along with polarimetric signatures of wet snow with sizes inferred to exceed 15 mm. A 2D-video disdrometer measured frequent occurrences of large drops >5 mm and combined with an optical array probe the drop size distribution was well-documented in spite of uncertainty for drops <0.5 mm due to high wind gusts and turbulence. The 1D McSnow control run and four numerical “experiments” were conducted and compared with observations. One of the main findings is that even at the moderate rain rate of 10 mm/h collisional breakup is essential for the shape of the drop size distribution.

The COVID-19 pandemic has disrupted the supply chain for personal protective equipment (PPE) for medical professionals, including N95-type respiratory protective masks. To address this shortage, many have looked to the agility and accessibility of additive manufacturing (AM) systems to provide a democratized, decentralized solution to producing respirators with equivalent protection for last-resort measures. However, there are concerns about the viability and safety in deploying this localized download, print, and wear strategy. Several polymer-based AM processes produce porous parts, and inherent process variation between printers and materials also threaten the integrity of tolerances and seals within the printed respirator assembly. The goal of this paper is to quantitatively measure particle transmission through printed respirators of different designs, materials, and AM processes, and assess the viability of printed respirators as N95 equivalents. Results from this study show that respirators printed using desktop/industrial-scale fused filament fabrication processes and industrial-scale powder bed fusion processes have insufficient filtration efficiency at the size of the SARS-CoV-2 virus, even while assuming a perfect seal between the respirator and the user’s face. Almost all printed respirators provided <60% filtration efficiency at the 100-300 nm particle range. Only one respirator, printed on an industrial-scale fused filament fabrication system provided >90% efficiency as-printed. Post-processing procedures including cleaning, sealing surfaces, and reinforcing the filter cap seal generally improved performance, but no respirator sustained the filtration efficiency of an N95 respirator, which filters 95% of SARS-CoV-2 virus particles. Instead, the printed respirators showed similar performance to various cloth masks. While continued optimization of printing process parameters and design tolerances could be implemented to directly print respirators that provide the requisite 95% filtration efficiency, AM processes are not sufficiently reliable for widespread distribution and local production of N95-type respiratory protection without commensurate quality assurance processes in place. Certain design/printer/material combinations may provide sufficient protection for specific users, but the respirators should not be trusted without quantitative filtration efficiency testing. It is currently not advised to expect printed respirators originating from distributed designs to replicate performance across different printers and materials.

An analysis is presented of the possible existence of a second anomalous dipole moment of Dirac’s particle next to the angular one. It includes a discussion why, in spite of his own derivation, Dirac has doubted about its relevancy. It is shown why since then it has been overlooked and why it has vanished from leading textbooks. A critical survey is given on the reasons of its reject, including the failure of attempts to measure and the perceived violations of time reversal symmetry and chargeparity symmetry. It is emphasized that the anomalous electric dipole moment of the pointlike electron (AEDM) is fundamentally different from the quantum field type electric dipole moment of an electron (eEDM) as defined in the standard model of particle physics and that its measurement requires different instrumentation. A proposal has been described how to prove or disprove its existence by experiment. Moreover, by reference from literature, the possible impact is discussed in the nuclear domain and in the gravitational domain.

Influence of particle size on the mechanical strength of alkali activated material from waste refractory monolithic was investigated in this study. Precursor was chemically and mineralogically analysed, separated on 4 fractions and alkali activated with Na-water glass. Alkali activated materials were thoroughly investigated under SEM and XRD to evaluate the not predicted differences in mechanical strength. Time dependence of curing temperature on mechanical strength was investigated in the sample prepared from a fraction that caused the highest compressive strength.

Indonesia's SUMBAGUT 150 kV transmission of High Voltage Alternating Current Network (HVAC) system has considerable power losses. These power losses are a critical problem in the transmission network system. Meanwhile, this study provides one solution to reduce power losses using a High Voltage Direct Current (HVDC) network system. Determining the location to convert HVAC into HVDC is very important. The authors use Particle Swarm Optimization (PSO) to get the optimal location on the 150 kV SUMBAGUT HVAC transmission network system. The study results showed that before using the HVDC network system, the power losses were 122.26 MW. Meanwhile, power losses with one transmission HVDC in the "Paya Pasir-Sei Rotan" are 84.16 MW, "Porsa-P. Siantar" 90.83 MW, "Paya Pasir-Paya Geli" 104.14 MW. Then power losses with two transmission HVDC in "Paya Pasir-Sei Rattan" and "Porsa-P. Siantar" is 71.24 MW, "Paya Pasir-Sei Rotan" and "Paya Pasir-Paya Geli" 77.46 MW, "Porsa-P. Siantar" and "Paya Pasir-Paya Geli" 78.52 MW. The last result, power losses with three transmission HVDC in "Paya Pasir-Sei Rotan," "Porsa-P. Siantar," and "Paya Pasir-Paya Geli" lost 64.57 MW.

Background: Identifying genetic mutations that lead to spermatogenic impairment is essential for offering patients informed genetic counseling and preventing the transmission of genetic defects to their offspring, often through the use of advanced reproductive technologies. Objectives: The purpose of this study was to identify potential single nucleotide polymorphisms (SNPs) associated with male infertility. Methods and Materials: Genetic variants that may cause infertility are identified by combining targeted next generation sequencing (NGS) panel and whole exome sequencing (WES). The validation step of Sanger sequencing adds confidence to the identified variants. Results: Our analysis revealed five distinct affected genes covering seven SNPs: based on targeted NGS panel and WES data, namely SPATA16 (rs16846616, 1515442, 1515441), CFTR (rs213950), KIF6 (rs2273063), STPG2 (r2903150) and DRC7 (rs3809611). Infertile men have a higher mutation rate than fertile men, especially those with azoospermia. Discussion and Conclusions: These findings strongly support the hypothesis that dysfunction of microtubule-related and spermatogenesis-specific genes contributes to idiopathic male infertility. The SPATA16, CFTR, KIF6, STPG2 and DRC7 mutations are associated with male infertility, specifically azoospermia, further examination of this genetic function is required.

The preparation of sulfur-single walled carbon nanohorns active material via a simple and sustainable evaporation method for application as cathode in lithium-sulfur batteries is reported. We show that the synthesis process enables the infiltration of elemental sulfur within the carbon nanohorns thus obtaining a morphology responsible for the ameliorating of the shuttle effect. The sulfur-carbon composite is characterized in terms of structure, morphology, and composition through x-ray diffraction, transmission electron microscopy, and thermogravimetric analyses. From the electrochemical point of view, cyclic voltammetry, rate capability, and galvanostatic cycling tests are performed employing a solution of bis(trifluoromethane)sulfonimide lithium salt and lithium nitrate in a mixture of 1,2-dimethoxyethane and 1,3-dioxolane in order to evaluate the electrode design applicability within lithium-sulfur cells. In this respect, further insights are provided by the estimation of the lithium-ion diffusion coefficient through the Randles-Sevcik equation, and by electrochemical impedance spectroscopy. The obtained results reveal a remarkable cycle life lasting around 800 cycles with a stable capacity of 520 mA h g-1 for the first 400 cycles at C/4, while reaching a value around 300 mA h g-1 at the 750th cycle. These results suggest sulfur-carbon nanohorns active material as a potential candidate for the next-generation battery technology.

The expansion of the universe yields two consistent differing values of a Hubble constant depending on the methods of measurement. The aim of this work was to explain the Hubble tension by properties of space corresponding to a radially inhomogeneous metrics. A cosmological model described by this perturbed FLRW metric has zero pressure and exponentially expanding space. But the observed space appears to be compressed in the radial direction. We consider the dependence of the change rate of photon energy on the direction of its motion. It is found applying the principle of the photon's energy integral extremum and the relationship between it and the energy of a material particle obtained using Lagrange mechanics. The change rate of a photon's energy varies depending on whether it moves in a radial direction or has an angular component. The metric coefficients are determined by the difference in measurements of the Hubble constant using gravitational lensing and a distance ladder.

This paper presents a multifunctional reflective digital metasurface of arbitrary base based on voltage tunable liquid crystal (LC). The reflective digital metamaterial can be multiplexed for different desirable functions by properly biasing the LC for different code patterns. Simulation results of three significant functions, beam steering with a steering elevation angle 27° at 75 GHz, RCS reduction of at least 15dB along the incident direction, and beam shaping with different beam shapes have been presented to prove the concept.

This paper presents a novel approach of welding thick steel plates that offers time and energy savings compared with conventional techniques. The combination of gas metal arc welding (GMAW) and hot-wire technology simplifies the joint configuration and enhances the process tolerance. In this study, a square butt joint was prepared with as-cut edges and a thickness of 15 mm. The relationship between the welding current and the deposition rate of solo GMAW showed limitations and low process tolerance. Increasing the welding current led to a larger deposited volume with unnecessary weld penetration. An independent deposition volume due to hot-wire insertion was used to improve process tolerance. This approach provided an additional volume without increasing the welding current and reduced unnecessary penetration. With optimized parameters, full-penetration single-pass welding was achieved. Compared with the formation of a typical single-v butt joint at a similar welding speed of 30 cm/min, the proposed process reduced the minimum arc time and power consumption by approximately 83% and 62%, respectively. Moreover, a single pass at a travel speed of 60 cm/min was achieved with approximately 91% and 81% less arc time and power consumption, respectively. In summary, the combined process simplifies the joint configuration, enables full-penetration single-pass welding, and reduces time and energy requirements.

Abstract: Composite sintered bodies comprising silicon dioxide (SiO2) nanoparticles dispersed in β-tricalcium phosphate (β-TCP) were prepared. The addition of nano-sized colloidal SiO2 to the β-TCP produced well-dispersed secondary phase nanoparticles that promoted densification by suppressing grain growth and increasing volume shrinkage of the sintered bodies. The SiO2 was found not to react with the β-TCP at 1120 °C and the substitution of silicon for phosphorous to produce a solid solution did not occur. This lack of a reaction is ascribed to the absence of avail-able calcium ions to compensate for the increase in charge associated with this substitution. The SiO2 nanoparticles were found to be present near the intersections of grain boundaries in the β-TCP. A β-TCP composite sintered body containing 4.0 wt% SiO2 exhibited a bending strength comparable to that of cortical bone and hence could potentially be used as a bone filling material.

With global warming and accelerated degradation of permafrost, the engineering problems caused by the formation of weak zones between the shallow and permafrost layers of soil–rock mixture (S-RM) slopes in permafrost regions have become increasingly prominent. To explore the influence of rock content on the shear strength of the S-RM freezing–thawing interface, the variation in the shear strength for different rock content is studied herein using direct shear tests. In addition, a 3D laser scanner is used for obtaining the topography of the shear failure surface. Combined with the analysis results of the shear band-particle calculation model, the influence of the rock content on the shear strength of the interface is explored. It was found that the impact threshold of the rock content on the interface strength and failure mode is approximately 30%, when the rock content (R) is &gt; 30% and that the shear strength increases rapidly with increasing rock content. When R ≤ 30%, the actual shear plane is similar to waves; when R &gt; 30%, the shear plane appears as gnawing failure. The shear strength of S-RM freezing–thawing interface mainly comes from the bite force and friction between particles. The main reason for the increase in shear strength with increasing rock content is the increase in bite force between particles, which makes the ratio of bite force to friction force approximately 1:1.

To address the severe distresses of asphalt pavement, a new type of pavement maintenance treatment, porous ultra-thin overlay (PUTO) with small particle size was proposed. The PUTO has a thickness of 1.5~2.5 cm and a large void ratio of 18~25%. As a newly asphalt mixture, the structure characteristics differ from traditional pavement. Therefore, it is necessary to investigated the fabrication schemes in laboratory and on-site, respectively. In this study, the optimal fabrication schemes, including compaction temperature and number of blows of PUTO were determined based on Cantabro test and volumetric parameters. Then, the corresponding relationship between laboratory and on-site compaction work was then established based on the energy equivalent principle. On this basis, the numbers of on-site rolling passes and the combination method were calculated. The results show that increased compaction temperature and number of blows reduce the height and enhance the compactness of the Marshall sample. With the same temperature and number of blows, the scattering resistance of coarse gradation (PAC-1) is better than that of fine gradation (PAC-2), and the increased asphalt viscosity significantly improves the scattering resistance of the asphalt mixture. To ensure the scattering resistance and volumetric characteristic, the initial compaction temperature of the PAC-1 and PAC-2 should not be lower than 150 °C and 165 °C, respectively. Then, the laboratory compaction work and on-site compaction work were calculated and converted based on the principle of energy equivalence. Consequently, the on-site compaction combination of rolling machines for four asphalt mixtures was determined. According to the volumetric parameters, the paving test section proved that the construction temperature and the on-site rolling combination determined by laboratory tests are reasonable, and ultra-thin overlay has good structural stability, drainage and skid resistance.

In this work, the use of graphite-like carbon nitride (g-C3N4) with improved texture characteristics for the synthesis of supported palladium catalysts of dehydrogenation of nitrogen-containing heterocycles was studied. This process is key to the creation of liquid organic carrier technology (LOHC) using N-heterocycles as reversibly hydrogenated/dehydrogenated substrates. For the preparation of g-C3N4-mca supports with advanced textural characteristics, well-established technology of the melamine cyanurate complex carbonization and standard techniques of adsorption precipitation together with wet impregnation were used for the synthesis of Pd-containing systems. The activity of the synthesized catalysts was studied in decahydroquinoline dehydrogenation. The high weight content of extractable hydrogen (7.2 wt %) and the high extraction rate, respectively, make it possible to consider these substances as the most promising N-heterocyclic compounds for this technology. It was shown that an increase in the specific surface area of g-C3N4 allows for achieving a slightly lower, but comparable fineness of palladium particles for the 1 wt% Pd/MCA-500 sample, compared to the standard 1 wt% Pd/C. In this case, the catalytic activity of 1 wt% Pd/MCA-500 in dehydrogenation of both substrates exceeded the analogous parameter for catalysts supported by nitrogen free supports. This regularity is presumably associated with the electron-donor effect of surface nitrogen, which favorably affects the dehydrogenation rate as well as the stability of catalytic systems.

The mutualistic interactions between the oleaginous yeast Lipomyces starkeyi and the green mi-croalga Chloroidium saccharophilum in mixed cultures were investigated to exploit possible syner-gistic effects. As a matter of facts, microalga could act as an oxygen generator for the yeast, while the yeast could provide carbon dioxide to microalga. A lignocellulosic hydrolysate from steam exploded Arundo donax (Giant reed) was used as low cost feedstock. The overall lipid content and lipid productivity obtained in the mixed culture treating the hydrolysate of Arundo donax were equal to 0.081 glipid.gbiomass-1 and 37.2 mglipid.L-1.d-1, respectively. They represented promising re-sults if compared to the model systems where synthetic media were used. This study provided new input for the integration of Single Cell Oil (SCO) production with agro-industrial feedstock and the fatty acid distribution mainly consisting of stearic (C18:0) and oleic acid (C18:1) allows promising applications in biofuels, cosmetics, food additives and other products of industrial interest.

In this paper, a kind of single-walled carbon nanotube nonlinear model is developed, and the strongly nonlinear dynamic characteristics of such carbon nanotubes subjected to random magnetic field are studied. The nonlocal effect of microstructure is considered based on the theory of nonlocal elasticity. The natural frequency of the strongly nonlinear dynamic system is obtained by the energy function method, the drift coefficient and the diffusion coefficient are verified. The stationary probability density function of the system dynamic response is given and the fractal boundary of the safe basin is provided. Theoretical analysis and numerical simulation show that stochastic resonance occurs when varying the random magnetic field intensity. The boundary of safe basin has fractal characteristics and the area of safe basin decreases when the intensity of the magnetic field permeability increases.

In this paper, we discussed the motion of neutral and charged particles in Schwarzschild black hole immersed in an electromagnetic universe (SEBH). The SEBH represents a mass M coupled to an external electromagnetic field that was derived from the metric of colliding electromagnetic waves with double polarization. Our study examine how the external electromagnetic field affects the trajectory of neutral and charged particles. First, the trajectories of both photon and test particle are studied. Then, the exact solutions for the trajectories are obtained in terms of the Jacobi-elliptic integrals for all possible energy and angular momentum of test particle and photon. We have also discussed the motion of charged particle and obtained the radius of the innermost circular orbit (ISCO). It is especially interesting when the charge sign is positive since this creates a conflict between gravitational (attraction force) and electric (repulsion force). Furthermore, we discuss the Lyapunov exponent and the effective force influencing particles in SEBH spacetime.

Wireless Sensor Networks (WSNs) has the characteristics of large-scale deployment, flexible networking, and wide application. It is an important part of the wireless communication networks. However, due to limited energy supply, the development of WSN is greatly restricted. Wireless Rechargeable Sensor Networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a joint optimization strategy is proposed to improve the energy management efficiency for WRSNs. The joint optimization strategy is divided into two phases. In the first phase, we design an Annulus Virtual Force based Particle Swarm Optimization (AVFPSO) algorithm for area coverage planing. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a Queuing Game-based Energy Supply (QGES) algorithm is designed for energy scheduling. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy, and extends the lifetime of WSN.

For children born with congenital heart defects (CHD), extracorporeal life support may be necessary. This study aimed to investigate the outcomes of children on extracorporeal membrane oxygenation (ECMO), focusing on various risk factors, including the type of CHD. The study was conducted as a retrospective single-center analysis at the Children's Hospital Zurich. All patients with CHD who required ECMO (veno-arterial or veno-venous) between 2009 and 2019 were included. Among the 88 patients, 36 (41%) had a single ventricle heart defect while 52 (59%) had a biventricular heart defect. Out of the total patients, 25 (28%) survived, with 7 (8%) having a single ventricle heart defect and 18 (20%) having a biventricular heart defect. Statistical analysis with a p-value of 0.19 revealed no significant difference in survival rates between the two groups. At the one-year follow-up, all children who survived to discharge were still alive. The rate of complications on ECMO was higher in children with a single ventricle (odds ratio [OR] 1.57, 95% confidence interval [CI] 0.67 - 3.7). The occurrence of a second ECMO run was more frequent in patients with a single ventricle compared to those with a biventricular physiology (22% vs. 9.6%). Although the study did not identify a significant difference in survival rates, it did indicate that patients with a single ventricle faced a higher risk of complications during ECMO and a greater likelihood of requiring a second ECMO run.

A single coronary artery is a very rare condition, commonly associated with other congenital anomalies. It could be generally classified as neither benign nor malignant form of congenital coronary artery anomalies since its pathophysiological and clinical implications grossly depend on different anatomical patterns defined by the site of origin and distribution of the branches. By presenting the patient with an isolated single coronary artery, who underwent successful combined aortic valve replacement and coronary artery bypass grafting surgery, we intend to distinguish casual from causal in this extremely rare clinical and surgical scenario. This is the first-ever case published, combining such underlying pathology, clinical presentation, and surgical treatment.

Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization methodologies to the main polymer processing operations. The most important characteristics related with the usage of optimization techniques, such as the nature of the objective function, the type of optimization algorithm, the modelling approach used to evaluate the solutions, and the parameters to optimize, are discussed. The aim is to identify the most important features of an optimization system for polymer processing problems, and define the best procedure for each particular practical situation. For this purpose, a state-of-the-art of the optimization methodologies usually employed is first presented, followed by an extensive review of the literature dealing with the major processing techniques, the discussion being completed by considering both the characteristics identified and the available optimization methodologies. This first part of the review focus on extrusion, namely extruders, extrusion dies and calibrators. It is concluded that there is a set of methodologies that can be confidently applied in polymer processing, with a very good performance and without the need of demanding computation requirements.

The requirement for integration of power plants due to the cyclical rise in electrical energy consumption is due to fluctuating load demand with the current grid systems. This integration necessitates effective allocating loads to the power plants for a minimum grid-tied transmission line cost while meeting network constraints. In this paper, we formulate an optimisation problem of minimising the total operational cost of all committed plants transmitted to the grid while meeting network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed Particle Swarm Optimization (PSO) method resolve the optimisation problem using piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction with connected load of 850MW, 1263MW and 2630MW power demand are contrasted with previous PSO application cost values, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), (73.86%, 89.10) respectively, and significant environmental-benefit. The proposed co-optimisation approach can enhance a significant self-consumption ratio compared to the baseline method.

Planck’s constant and the gravitational constant are comprised of more fundamental quantities of length, mass, and time. Reformulating traditional equations in terms of these fundamental units offers a more granular view of the physical transformations encoded in the equations of physics. The composite structure of h and G conceals a simple model in which maximum unit potentials are reduced by dimensionless proportionality operators. Natural symmetries correlate the three unit dimensions, yielding predictable quantities of physical dynamics. Insights are organized into a New Foundations Model of physics that reformulates traditional constants and equations in elementary form. The New Foundations Model offers a common language for describing quantum mechanical, gravitational, and electromagnetic phenomena.

The emerging use of visualization techniques in pathology and microbiol- ogy has been accelerated by machine learning (ML) approaches towards image preprocessing, classification, and feature extraction in an increasingly complex series of datasets. Modern Convolutional Neural Network (CNN) architectures have developed into an umbrella of vast image reinforcement and recognition methods, including a combined classification-localization of single/multi-object featured images. As a subtype neural network, CNN cre- ates a rapid order of complexity by initially detecting borderlines, edges, and colours in images for dataset construction, eventually capable in mapping intricate objects and conformities. This paper investigates the disparities between Tensorflow object detection APIs, exclusively, Single Shot Detector (SSD) Mobilenet V1 and the Faster RCNN Inception V2 model, to sample computational drawbacks in accuracy-precision vs. real time visualization capabilities. The situation of rapid ML medical image analysis is theoretically framed in regions with limited access to pathology and disease prevention departments (e.g. 3rd world and impoverished countries). Dark field mi- croscopy datasets of an initial 62 XML-JPG annotated training files were processed under Malaria and Syphilis classes. Model trainings were halted as soon as loss values were regularized and converged.

Single crystals of quaternary adamantine type Cu•GaGeS4 were grown by chemical vapor transport technique using iodine as transport agent. Dark red transparent crystals grew in a temperature gradient of dT = 900–750 °C. Chemical characterization by X-ray fluorescence showed an off-stoichiometric composition of the Cu•GaGeS4 crystals, in particular a slight Ge-deficit. By X-ray diffraction, Cu•GaGeS4 was found to adopt the chalcopyrite-type structure with the space group I-42d. The cation distribution in this structure was analyzed by multiple energy anomalous synchrotron X-ray diffraction and it was found that Cu and vacancies occupy the 4a site, whereas Ga and Ge occupy the 4b site. The band gap energies of several off-stoichiometric Cu•GaGeS4 crystals were determined by UV-Vis spectroscopy and are in the range of 2.1 to 2.4 eV. A non-linear correlation of the band gap energy with the Ge-content of the compound follows the usual bowing behaviour of semiconductor alloys with a bowing parameter of b = 1.45(0.08).

The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and generate products with better properties. The present review aims at identifying and discussing the most important characteristics of polymer processing optimization problems in terms of the nature of the objective function, optimization algorithm, and process modelling approach that is used to evaluate the solutions and the parameters to optimize. Taking into account the research efforts developed so far, it is shown that several optimization methodologies can be applied to polymer processing with good results, without demanding important computational requirements. Also, within the field of artificial intelligence, several approaches can be reach significant success. The first part of this review demonstrated the advantages of the optimization approach in polymer processing, discussed some concepts on multi-objective optimization and reported the application of optimization methodologies in single and twin screw extruders, extrusion dies and calibrators. This second part focus on injection molding, blow molding and thermoforming technologies.

Proteasomes critically regulate proteostasis via protein degradation. Proteasomes are multisubunit complexes composed of the 20S proteolytic core particle (20S CP) that, in association with one or two 19S regulatory particles (19S RPs), generate the 26S proteasome. 26S is the major proteasomal complex in the cells. Native gel protocols are used to investigate the 26S/20S ratio. However, a simple method for detecting these proteasome complexes in the cells is missing. To this end, using CRISPR technology, we YFP tagged the endogenous PSMB6 (b1) gene, a 20S CP subunit, and co-tagged endogenous PSMD6 (Rpn7), a 19S RP subunit, with mScarlet fluorescent protein. We observed colocalization of YFP and mScarlet fluorescent proteins in the cells, with higher nuclear accumulation. Nuclear proteasomal granules are formed under osmotic stress, and all were positive for YFP and mScarlet. Previously we have reported that PSMD1 knockdown, one of the 19 RP subunits, gives rise to a high level of “free” 20S CP. Intriguingly, under this condition, the 20S-YFP remained nuclear, whereas the PSMD6-mScarlet is mostly in cytoplasm, demonstrating the distinct subcellular distribution of uncapped 20S CP. Lately, we have shown that the PSMA3 (a7) C-termininus, a 20S CP subunit, binds multiple intrinsically disordered proteins (IDPs). Remarkably, truncation of the PSMA3 C-terminus is phenotypically reminiscent of PSMD1 knockdown. These data suggest the PSMA3 C-terminal region is critical for the 26S proteasome integrity.

Low-cost particulate matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and their cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors but some models of sensors also report particle number concentrations (PNC) segregated into different PM size bins. In this study, 8 units of each Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors to different particle size distributions to quantify and better understand the low cost sensors performance across a range of relevant environmental ranges. The PNC reported by the sensors were analysed. This study showed that there is added value in directly using the PNC reported by the sensors instead of the mass concentrations, which could aid the efforts to calibrate these sensors to a known accuracy. It demonstrated that all sensors tested here could track the fine temporal variation of PNC, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it showed that particle size distribution and composition are more impactful on the sensors measurement than relative humidity.

This paper is regarding design and program an Micro-controller Arduino Uno board by using Arduino software to work as a photo-sensor(Active) single axial solar tracker system(SASTS). A solar panel, two photo-resistors (LDR) in two sides (north/south) of the photo-voltaic(PV) and a servo motor are connected to the Uno board, which is running a code that prepared by Arduino software IDE in advanced then it works as a tracking system. Here, the LDRs send the signal of presence or absence of the light to the board and based on that sent signal the Uno reflects a new signal to the servo motor to rotate and finds the light source. Lastly, the photo-sensor single axis tracker is made while Continuously, the system tries to face the panel to the sun and whilst changing the irradiance intensity it starts searching to find the angle of highest irradiance. Based on results that are extracted from the data, the tracker system significantly boosts the output efficiency of the solar panel. By using the Micro-controller Uno board, LDRs, servo motor and special designed mechanical base, the tracking system is constructed, based on acquired data the influence of the STS on the increasing the solar panel efficiency is more obvious. Significantly, the tracker system rises the efficiency of the PV .

The optical reflection characteristics of asphalt pavement are an important influencing factor in road lighting design, and the macro and micro textures of asphalt pavement significantly affect its reflection characteristics. To investigate the impact of texture parameters on the retroreflection coefficient of asphalt pavement, this study obtained the macroscopic and microscopic texture properties of rutting board specimens and on-site asphalt pavement using a pavement texture tester. The macro- and microtexture parameters, such as macroscopic texture distribution density (D1), microscopic texture distribution density (D2), profile height root-mean-square (Rq), profile slope root-mean-square (Δq), skewness Rsk and kurtosis Rku, were measured, and the corresponding retroreflection coefficient RL was measured using a retro-reflectometer. In the laboratory ex-periments, rutting specimens of AC-13, SMA-13, and OGFC-13 asphalt mixtures were formed. The changes in texture parameters and retroreflection coefficient of different rutting specimens before and after crushing were studied, and a factor influence model between macro- and microtexture parameters and RL was established. And the correlation models of texture index and RL of asphalt pavement are further established. The results showed that in the single factor model, the param-eters can be used to characterize RL with high prediction accuracy, whereas for the on-site meas-urements, the three parameters of Δq, Rsk, and Rku can characterize the RL well. The nonlinear model established on the basis of the B-P neural network algorithm improves its prediction ac-curacy. This research can provide ideas for optimizing the reflection characteristics of asphalt pavement and further provide a decision-making basis for road lighting design.

To guide analysts to select the right tool and parameters in differential gene expression analysis of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline (https://github.com/interactivereport/scRNAseq_DE) for single cell analysis will enable scientists to better understand cell-type specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations of filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.

Pyridoxine is a versatile molecule that forms part of the family of B vitamins. It is used to treat and prevent vitamin B6 deficiency and certain type of metabolic disorders. Besides, pyridoxine molecule has been investigated as a suitable ligand toward metal ions. Nevertheless, the study of the magnetic properties of metal complexes containing lanthanide(III) ions and this biomolecule has been unexplored. We have synthesized and characterized a novel pyridoxine-based GdIII complex of formula [Gd(III)(pyr)2(H2O)4]Cl3 • 2 H2O (1) [pyr = pyridoxine]. 1 crystallizes in the triclinic system with space group Pī. In its crystal packing, the cationic [Gd(pyr)2(H2O)4]3+ entities are linked each other through H-bonding interactions involving non-coordinating water molecules and chloride anions. In addition, Hirshfeld surfaces of 1 were calculated to further investigate their intermolecular interactions in the crystal lattice. The study of the magnetic properties of 1 through ac magnetic susceptibility measurements reveals the occurrence of slow relaxation of magnetization in this mononuclear Gd(III) complex, this fact indicating an unusual single-ion magnet (SIM) behavior. We have also studied the relaxometric properties of 1, as a potential contrast agent for high-field MR imaging, from solutions of 1 prepared in physiological serum (0.0 - 3.2 mM range) and measured at 3 T on a clinical MR scanner. The relaxivity values obtained for 1 are larger than those of some commercial MR imaging contrast agents based on mononuclear Gd(III) systems

[Objective] To investigate the expression of ALDH2 in hepatocellular carcinoma and its correlation with macrophage infiltration and with the activation of specific subtypes of macrophages. [Methods] Transcriptome and clinical data from patients with hepatocellular carcinoma were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases and analyzed and collated using R and Perl software. In the TCGA hepatocellular carcinoma dataset, the Wilcoxon test was used to identify macrophage activation-related gene sets that show differential expression between hepatocellular carcinoma and normal tissues. Single-cell sequencing combined with bulk data was used to analyze macrophage infiltration of hepatocellular carcinoma immune cells and the activation and recruitment of different subtypes of macrophages. Single-factor Cox regression and minimum absolute convergence and selection operator (Lasso) were used to identify survival-related genes and construct prognostic models of HCC. The patients were divided into two groups based on their model scores. The Wilcoxon test was used to identify differentially expressed genes (DEGs) between the two groups, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses of the DEGs were performed. Single-sample gene set enrichment analysis (ssGSEA) was performed to explore the possible mechanism underlying the differences in prognosis. The reliability of the model was verified using the ICGC hepatocellular carcinoma dataset. A Kaplan‒Meier curve was constructed and used to analyze the influence of macrophage activation-related gene expression on survival and prognosis in patients with hepatocellular carcinoma. The log-rank test and the independent t test were used to analyze survival time data and to compare immune cell infiltration in the two groups. [Results] In a comparison of hepatocellular carcinoma and paracancerous tissues, the expression of six of 137 macrophage activation-related genes (ALDH2, CCNB2, CYP2C9, CYP3A4, F9 and KLKB1) was found to be significantly correlated with overall survival time (OS) in patients with hepatocellular carcinoma. Based on this finding, a prognosis assessment model for hepatocellular carcinoma was constructed. The OS of the patients in the high-scoring group was significantly shorter than the OS of the patients in the low-scoring group (P<0.05), and the model predicted the prognosis of patients with hepatocellular carcinoma independently of sex, age, tumor grade and stage. The area under the receiver operating characteristic (ROC) curve of the model used to predict 1-, 2-, and 3-year survival was greater than 0.7 in both the test set and the validation set. GO enrichment of DEGs between the high- and low-scoring groups of the model suggested that the DEGs are mainly related to immune function. GSEA suggested that the tumor-infiltrating immune cells in the high-scoring group were macrophages, Th2 cells and Treg cells and showed that the number of tumor-infiltrating immune cells, including NK cells, was lower in the high-scoring group than in the low-scoring group. With respect to immune function, the immune checkpoint of the high-scoring group was higher than that of the low-scoring group. Single-cell sequencing data indicated the recruitment of B cells, M0 cells, M1 cells and M2 cells to the hepatocellular carcinoma microenvironment and suggested that ALDH2 regulates related signaling pathways in a way that promotes the differentiation of macrophages into M2-type macrophages. [Conclusion] M2-type macrophages are the dominant type of macrophages associated with immune infiltration in hepatocellular carcinoma. The macrophage-associated activation gene ALDH2 activates the macrophage polarization signaling pathway and negatively regulates the differentiation of macrophages into M2-type macrophages.

The development of the ovarian antral follicle is a complex, highly regulated process. Oocytes orchestrate and coordinate the development of mammalian ovarian follicles, and the rate of follicular development is governed by a developmental program intrinsic to the oocyte. Characterizing oocyte signatures during this dynamic process is critical for understanding oocyte maturation and follicular development. Although the transcriptional signature of sheep oocytes matured in vitro and preovulatory oocytes have been previously described, the transcriptional changes of oocytes in antral follicles have not. Here, we used single-cell transcriptomics (SmartSeq2) to characterize sheep oocytes from small, medium, and large antral follicles. We characterized the transcriptomic landscape of sheep oocytes during antral follicle development, identifying unique features in the transcriptional atlas, stage-specific molecular signatures, oocyte-secreted factors, and transcription factor networks. Notably, we identified specific gene and signaling pathways in each antral follicle stage that may reflect oocyte quality and in vitro maturation competency. Additionally, we discovered key biological processes that drive the transition from small to large antral follicles, revealing hub genes involved in follicle recruitment and selection. Thus, our work provides a comprehensive characterization of the single-oocyte transcriptome, filling a gap in the mapping of the molecular landscape of sheep oogenesis. We also provide key insights into the transcriptional regulation of the critical stages of antral follicular development, which is essential for understanding how the oocyte orchestrates follicular development.

Single-Molecule Magnets (SMMs) have attracted much interest because of their spin properties and potential technological applications. Besides, a great effort has been devoted to their functionalization, which is made with functional groups convenient to connect suitable SMMs to junction devices or to perform their grafting on surfaces of different substrates. We have synthesized and characterized two lipoic acid-functionalized and oxime-based MnIII compounds, of formula [Mn6(μ3-O)2(H2N-sao)6(lip)2(MeOH)6][Mn6(μ3-O)2(H2N-sao)6(cnph)2(MeOH)6]}·10MeOH (1) and [Mn6(μ3-O)2(H2N-sao)6(lip)2(EtOH)6]·EtOH·2H2O (2) [H2N-saoH2 = salicylamidoxime, lip = lipoate anion, cnph = 2-cyanophenolate anion]. 1 crystallizes in the triclinic system with space group Pī and 2 crystallizes in the monoclinic system with space group C2/c. In their crystal packing, adjacent Mn6 complexes are connected through non-coordinating solvent molecules, which are H-bonded to N atoms of –NH2 groups of salicylamidoxime ligand. In addition, Hirshfeld surfaces of 1 and 2 were calculated to study the variety of intermolecular interactions and different level of importance that take place in their crystal lattice, this type of computed study being the first time performed on Mn6 molecules. The study of the magnetic properties of 1 and 2 through dc magnetic susceptibility measurements reveals the coexistence of ferro- and antiferromagnetic exchange couplings between the Mn(III) metal ions in both compounds, the latter being the predominant magnetic interaction. A ground state spin value of S = 4 was obtained through isotropic simulations of the experimental magnetic susceptibility data for both 1 and 2. Ac magnetic susceptibility measurements show the occurrence of slow relaxation of the magnetization in 1 and 2, which indicate that SMM behavior take place in both compounds.

With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need and significance for the development of novel image encryption algorithms is unprecedented. In this research work, we propose a novel framework for image encryption that is based on two hyperchaotic maps, utilized in conjunction with the single neuron model (SNM). The framework entails three successive stages, where in every stage, a substitution box (S-box) is applied, then XORing with an encryption key is carried out. The S-boxes and the encryption keys are generated from the numerical solutions of the hyperchaotic maps and the SNM. The performance of the proposed framework is gauged through a number of metrics, reflecting superior performance and complete asymmetry between the plain images and their encrypted versions. However, the main advantages of this work are: (1) The attained width of the key space; and (2) The achieved efficiency in software implementation. The superior key space of 2^{2551} is the result of employing the two hyperchaotic maps, while the improved efficiency, resulting in an average encryption rate of 8.54 Mbps, is the result of the utilization of the single neuron model, as well as the employment of optimized parallel processing techniques. In addition, the proposed encryption framework is shown to output encrypted images that pass the NIST SP 800 suite.

Type-2 diabetes mellitus (T2DM) have been strongly associated with single nucleotide polymorphisms (SNPs) in the TCF7L2 gene. This study investigated the association between rs12255372, rs7903146 and T2DM in a Ghanaian population. A case-control study design was used for this study. A total of 106 T2DM patients and 110 control participants were selected. Basic data collected included body mass index, blood pressure and socio-demographics. Fasting blood samples were collected and used for serum lipid analysis, HbA1c, plasma glucose estimation and DNA extraction. Common and allele-specific primers were designed for genotyping using the Modified Tetra-Primer Amplification assay. Associations were evaluated using logistic regression models. The rs7903146 risk variant was significantly associated with 2.16 vs 4.06 increased odds for T2DM in patients <60 years vs ≥60 years. Both rs7903146 and rs12255372 are significantly associated with increased odds of T2DM in women, overweight/obese; T2DM negative family history (T2DM-NFH) and low-HDL-C. In a multivariate model, rs7903146 but not rs12255372 was significantly associated with 2.18, 5.01 and 2.25 increased odds of T2DM, under the codominant, recessive and additive model, respectively (p<0.05). The association between rs7903146 and rs12255372 with T2DM is more highly associated in a subgroup- women and those with T2DM-NFH, yet have cardiometabolic risk.

This paper reports on the changing dynamics of a small town’s social-ecological system (SES) concerning oil and gas industry boom-bust economic cycles and both the vulnerability and resilience of the town over the past 30 years. With the goal to understand how resource-based single industry impact social-ecological systems, we developed indicators of human and environmental well-being and assessed them. Seven indicators include labor force distribution, education, oil price, household income, water quality, air quality, and land cover land use. Over this period, Drayton Valley, Canada quadrupled in size, with more than 20% of the population working in the oil and gas sector. Median income rose to 42% above the national average despite the population lagging national benchmarks for educational attainment. There have also been dramatic fluctuations in levels of fluoride, phosphorus, and other chemicals in water quality samples, implying a correlation with fossil fuel extractive activities over this period. Land cover land use change analysis shows a decreased area of water bodies, wetland, and forests, and increased built capital and agricultural land. While economic boom cycles have led to cash inflows, an exclusive focus on the benefits of the oil and gas industry may leave those dependent on the industry vulnerable to social and environmental risk factors during bust cycles that are beyond their control in the everchanging global oil economy. This phenomenon which has been referred to as the “resource curse” suggests the need to anticipate cyclical (or more sustained) periods of low levels of oil and gas production. These results suggest that single boom-bust economies impact every aspect of social-ecological systems. Therefore, a sustainable development plan that comprehensively considers not only economic growth, but also diversification, environment protection, and strategic land use planning is indispensable to ensure the long-term development of communities that depend upon extractive industries.

In the manufacturing industry, orders are typically scheduled and delivered through batches, and the probability of machine failure under high-load operation is high. On this basis, we focus on a single machine batch scheduling problem with a maintenance interval (SMBSP-MI). The studied problem is expressed by three-field representation as 1|B,MI|\sum{F_j+\mu}m, and the optimization objective is to minimize total flow time and delivery costs. Firstly, 1|B,MI|\sum{F_j+\mu}m is proved to be NP-hard by Turing reduction. Secondly, shortest processing time (SPT) order is shown the optimal scheduling of SMBSP-MI, and a dynamic programming algorithm based on SPT (DPA-SPT) with the time complexity of O(n^3T_1) is proposed. A small-scale example is designed to verify the feasibility of DPA-SPT. Finally, DPA-SPT is approximated to a fully-polynomial dynamic programming approximation algorithm based on SPT (FDPAA-SPT) by intervals partitioning technique. The proposed FDPAA-SPT runs in O(\frac{n^5}{\varepsilon^2})\ time with the approximation (1+\varepsilon).

This paper investigates whether optimisation methods with the population made up of one solution can suffer from structural bias just like their multisolution variants. Following recent results highlighting the importance of choice of strategy for handling solutions generated outside the domain, a selection of single solution methods are considered in conjunction with several such strategies. Obtained results are tested for the presence of structural bias by means of a traditional approach from literature and a newly proposed here statistical approach. These two tests are demonstrated to be not fully consistent. All tested methods are found to be structurally biased with at least one of the tested strategies. Confirming results for multisolution methods, it is such strategy that is shown to control the emergence of structural bias in single solution methods. Some of the tested methods exhibit a kind of structural bias that has not been observed before.

Magnetorheological elastomers (MREs) are a type of smart material that can change their mechanical properties in response to external magnetic fields. These unique properties make them ideal for various applications, including vibration control, noise reduction, and shock absorption. This paper presents an approach for modeling the impact behavior of MREs. The proposed model uses a combination of exponential functions arranged in a multi-layer Preisach model to capture the nonlinear behavior of MREs under impact loads. The model is trained using particle swarm optimization (PSO) and validated using experimental data from drop impact tests conducted on MRE samples under various magnetic field strengths. The results demonstrate that the proposed model can accurately predict the impact behavior of MREs, making it a useful tool for designing MRE-based devices that require precise control of their impact response. The model's response closely matches the experimental data, with a maximum prediction error of 10% or less. Furthermore, the interpolated model's response shows good agreement with the experimental data, with a maximum percentage error of less than 8.5%.

Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder that can lead to heart failure and sudden cardiac death, characterized at the histological level by focal areas of myocyte disarray, hypertrophy and fibrosis, and few disease-targeted therapies exist. To identify, focal, spatially restricted alterations in transcriptional pathways and reveal novel therapeutic targets, we performed a spatial transcriptomic analysis of areas of focal myocyte disarray compared to areas of normal tissue, using a commercially available platform (GeoMx, nanoString). We analyzed surgical myectomy tissue from four patients with HCM and control interventricular septum tissue from two unused organ donor hearts that were free of cardiovascular disease. Histological sections were reviewed by an expert pathologist and 72 focal areas with varying degrees of myocyte disarray (normal, mild, moderate, severe) were chosen for analysis. Areas of interest were interrogated with the Human Cancer Transcriptome Atlas designed to profile 1800 transcripts. Differential expression analysis revealed significant changes in gene expression between HCM and Control tissue, and functional enrichment analysis indicated these genes were primarily involved in interferon production and mitochondrial energetics. Within HCM tissue, differentially expressed genes between areas of mild and moderate disarray were enriched for genes related to mitochondrial energetics (moderate disarray) and response to oxygen/cytokine levels (mild disarray). The comparison between areas of moderate and severe disarray were enriched for genes related to the c-Jun N-terminal kinase (JNK) cascade in severe disarray. Analysis of ligand-receptor pair gene expression revealed that HCM tissue exhibited downregulation of platelet-derived growth factor (PDGF), NOTCH, junctional adhesion molecule, and CD46 signaling, while showing upregulation of fibronectin, CD99, cadherin, and amyloid precursor protein signaling. A deconvolution analysis utilizing the matched single nuclei RNA-sequencing (snRNA-seq) data to determine cell type composition in areas of interest revealed significant differences in fibroblast and vascular cell composition in areas of severe disarray when compared to normal areas in HCM samples. Cell composition in normal areas from control tissue was also divergent from normal areas in HCM samples, which was consistent with the differential expression results. Overall, our data identify novel and potential disease-modifying targets for therapy in HCM.

The phenomenon of interference is a characteristic of all waves. This paper reviews the interference of classical waves and of matter waves in quantum mechanics. In quantum mechanics, there are two interference mechanisms between identical particles. One is that the de Broglie wavelengths of two interference waves are the same, which is called the equal wavelength interference mechanism. The other is that the wavelengths of interference waves can also be different, in which case there should be an exchange term in additional to the direction term in the wave function. There is a fixed phase difference between the two terms, which causes interference. This is called exchange effect interference mechanism. We propose an experiment, which is simpler to what has been done, to show the exchange effect interference. There is a special case where two interference waves can satisfy both the two mechanisms. We propose an experiment to discriminate that in this case which mechanism plays a role on earth.

This study investigates the contribution of nano-alumina (nA: 1-3wt.% binder) to the performance of silica fume (SF-10%) blended mortar in ternary blended mortar. The fresh (workability, setting time) and hardened properties (compressive strength and thermal resistance (300 °C for 1 h)), were examined. Nano alumina (nA) contributed positively to the consistency of SF blended mortar such that similar workability could be achieved with OPC when 3% nA was added. The initial and final setting time of 180 and 220 mins were recorded in SF blended mortar and reduced by 18.4 and 21.8%, respectively by adding 3%nA. The optimum mixture that contained 10%SF and 2%nA had 3-d and 28-d compressive strengths of 28 and 43.2 MPa. In the absence of nA, this reduced by 14.3% and 29.4% while it reduced by 25.2% and 16.7%, respectively in OPC mortar. The main products characterizing SF-nA ternary blended binder with dense microstructural density were nepheline and tobermorite. Besides, it is more susceptible to carbonation despite its superior performance in thermal resistance when compared to SF blended and OPC mortars.

For randomly-moving-particle swarm, the past researches only focused on its whole behavior and few people have studied the special particle swarm formed in it, which leading to the phenomenon and reasons for the spontaneous aggregation of particles in the special particle swarm being still unknown. For such a special particle swarm, we have previously studied the causes of its special relativity phenomenon. Here we show the causes of spontaneous aggregation of “randomly moving” particles. The diffusion kinetics of particles in a special circumstance (that is, in a moving reference frame $\mathcal{R}_u$ relative to the stationary reference frame $\mathcal{R}_0$) are studied theoretically. For the first time, the effects of the location aggregation and velocity direction aggregation of randomly moving particles on the diffusion coefficient are considered, and the corresponding generalized diffusion equation is deduced employing concise mathematical logic and Mathematica software.

Background/Objectives: To assess the prevalence of diamine oxidase (DAO) enzyme deficiency caused by single nucleotide polymorphisms (SNPs) of the AOC1 gene in a sample of patients with symptoms of insomnia. Methods: A total of 167 adult patients (&gt; 18 years of age) with symptoms of insomnia attended in a specialized institute for healthy sleep, in Barcelona (Spain) between May and November 2023, underwent genotyping analysis of the four most relevant SNP variants, including c.691G&gt;7 (rs2052129), c.47C&gt;T (rs10156191), c.995C&gt;T (rs1049742), and c.1990C&gt;G (rs1049793). Results: Genetic DAO deficiency was present in 138 patients, with a prevalence rate of 82.6% (95% CI 76-88.1%). Difficulties in staying asleep was the most common complaint in 88% of patients followed by trouble falling asleep in 60.5%. More than half of patients suffered from insomnia symptoms every day. Also, 99.4% reported daytime consequences of insomnia, with fatigue (79.6%), mood changes (72.5%), and impaired concentration in 70.1%. When patients were grouped by DAO-score, which reflected the number of heterozygous and homozygous SNPs variants, the group with a DAO-score ≥ 4 vs. 1 showed higher percentages of insomnia-related symptoms, in particular trouble staying asleep and early morning awakening. These two symptoms were also more common in the presence of the c.1990C&gt;G (rs1049793) variant. Conclusions: This preliminary real-world study presents novel evidence of a potential link between DAO enzyme deficiency of genetic origin and clinical symptoms of insomnia may suggest the potential benefit of DAO supplementation to improve the quality of sleep in these subjects.

Semantic image segmentation has recently witnessed considerable progress by training deep convolutional neural networks (CNNs). The core issue of this technique is the limited capacity of CNNs to depict visual objects. Existing approaches tend to utilize approximate inference in a discrete domain or additional aides and do not have a global optimum guarantee. We propose the use of the multi-label manifold ranking (MR) method in solving the linear objective energy function in a continuous domain to delineate visual objects and solve these problems. We present a novel embedded single stream optimization method based on the manifold ranking (MR) model to avoid approximations without sacrificing expressive power. In addition, we propose a novel network, which we refer to as dual multi-scale manifold ranking (\textbf{DMSMR}) network, that combines the dilated, multi-scale strategies with the single stream MR optimization method in the deep learning architecture to further improve the performance. Experiments on high resolution images, including close-range and remote sensing datasets, demonstrate that the proposed approach can achieve competitive accuracy without additional aides in an end-to-end manner.

Tri-dimensional (3D) cell aggregates or spheroids are considered to be closer to physiological conditions than traditional 2D cell culture. Mesenchymal stem cells (MSCs) assembling in spheroids have increased the survival of transplanted cells. The regulation of the biological processes that maintain crucial physiological reactions of MSCs is closely related to the functioning of ion channels. The pattern of expression, role and regulatory mechanisms of ion channels could be significantly different in 3D compared to 2D culture, and, thus, needed to be properly analyzed on the level of ionic currents. We developed a specific approach that allowed us to register, for the first time, endogenous ion channels in endometrial MSCs (eMSCs) assembled in spheroids. Particularly, using the single-channel patch-clamp technique, we have recorded the activity of ion channels and observed their functional interplay in mechanosensitive clusters. Our experimental protocol could be applied for identification and studying of ion channels in 3D cell cultures.

The asymmetry in the dynamics of an electro-pneumatic actuating device consisting of an electro-pneumatic transducer and a single-action pneumatic actuator was unexpectedly found experimentally. This asymmetry manifests in response to large step excitations. The dynamic asymmetry effect is understood as a change in the shape of the response of an actuator depending on the direction of the actuators stem movement. The questions appears: How to explain this effect? Does this phenomenon reflect thermodynamic air processes? Is it connected with air-to-mechanical energy conversion? Together, six working hypotheses explaining this effect were formulated. The asymmetry was studied in detail using analytical and simulation modeling, as well as experimental research. In this respect, a nonlinear analytical model was developed, tuned, and later solved using simulations. The simulation model was verified based on the experiment data. In addition, the problem of the efficiency in the energy conversion of a single-action actuator was discussed and, in result, the maximum theoretical energy efficiency was determined. Subsequently, all six working hypotheses were verified. Finally, the hypothesis explaining asymmetry as an effect of the different thermodynamic air processes in both actuator’s stem travel directions was confirmed.

Organ-on-a-chip (OOC) is an emerging technology that simulates an artificial organ within a microfluidic cell culture chip. Current cell biology research focuses on in vitro cell culture due to various limitations of in vivo testing. Unfortunately, in vitro cell culture fails to provide an accurate microenvironment, and in vivo cell culture is expensive and has historically been a source of ethical controversy. OOC aims to overcome these shortcomings and provide the best of both in vivo and in vitro cell culture research. The key component of the OOC design is utilizing microfluidics to ensure a stable concentration gradient, dynamic mechanical stress modeling, and accurate reconstruction of a cellular microenvironment. OOC also has the advantage of complete observation and control of the system, which is impossible to recreate in in vivo research. Multiple throughputs, channels, membranes, and chambers are constructed in a polydimethylsiloxane (PDMS) array to simulate various organs on a chip. Various experiments can be performed utilizing OOC technology, including drug delivery research and toxicology. Current technological expansions involve multiple organ microenvironments on a single chip, allowing for studying inter-tissue interactions. Other developments in the OOC technology include finding a more suitable material as a replacement for PDMS and minimizing artefactual error and non-translatable differences.

CubeSats, known for their compact size and cost-effectiveness, have gained significant popularity. However, their limited size imposes restrictions on optical aperture and, consequently, Ground Resolution Distance in Earth Observation missions. To overcome this limitation, the concept of deployable optical payloads with segmented primary mirrors which can unfold like petals has emerged, enabling larger synthetic apertures and enhanced spatial resolution. This study explores the potential benefits of leveraging Additive Manufacturing (AM) and Topology Optimization (TO) in the realm of ultra-precision machining, specifically single point diamond machining. The goal is to reduce fixture weight while improving stiffness to minimize deformations caused by rotational and cutting forces which compromise optical performance. Through Finite Element Analysis, this research compares conventionally machined fixtures with those produced using AM and TO techniques. The results reveal that concept designs created through TO can achieve a remarkable 68% reduction in weight. This reduction makes the assembly, including the machining fixture and 12U deployable segments, manageable by a single operator without the need for specialized lifting equipment. Moreover, these innovative designs lead to substantial reductions of up to 86% and 51% in deformation induced by rotational and cutting forces, respectively.

The literature has well established prestressing in concrete beams and its calculation procedures. However, its use in composite steel-concrete beams has increased despite main normative codes lacking a specific approach for it. Designers must, in most cases, combine the available criteria to calculate the standards of steel and reinforced concrete structures. This study aimed to formulate this optimization problem and analyze CO2 emissions for the optimal design of composite steel-concrete beams with external prestressing. The design variables for the optimization problem include cross-section of laminated or welded profiles, slab height, characteristic strength of concrete to compression, and number of tendons. We obtained a solution for the optimization problem via a genetic algorithm (GA) and a particle swarm optimization one (PSO). Comparative analysis with experimental example and with optimization problem of prestressed steel beam are performed. An parametric analysis to several spams is performed, which enabled to evaluate the factors that most impact CO2 emissions. According to results, the chosen algorithms effectively obtained solutions for the problem, and PSO usually provided better results than GA. Regarding the final solution composition, steel contributes the most to emissions, welded profiles provided better solutions than laminated ones, and laminated spans of up to 17.5m and welded ones up to 27.5m dispensed with tendons.

Single nucleotide polymorphisms (SNP) are associated with diseases and drug response variabilities in humans. Elucidating the damaging and disease-associated SNPs using wet-laboratory approaches can be challenging and resource-demanding due to the large number of SNPs in the human genome. Due to the growth in the field of computational biology and bioinformatics, algorithms have been developed to help screen and filter out the most deleterious SNPs that are worth considering for wet-laboratory studies. Here we review the existing in-silico based methods used to predict and characterize the effects of SNPs on protein structure and function. This cutting-edge approach will facilitate the search for novel therapeutics, help understand the etiology of diseases and fast-track the personalized medicine agenda.

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study on the efficacy of Particle Swarm Optimization (PSO) combined with Histogram Equalization (HE) preprocessing for medical image segmentation, focusing on Lung CT-Scan and Chest X-ray datasets. Best Cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly in complex Lung CT-Scan images. Evaluation metrics, including Accuracy, Precision, Recall, F-Score, Specificity, Dice, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially in Chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach in advancing the accuracy and reliability of medical image segmentation, paving the way for further research and method integration to enhance this critical healthcare application.

This paper presents a novel approach for the recognition of multiple sclerosis (MS) using wavelet entropy and a Particle Swarm Optimization (PSO)-based neural network. MS is a complex neurological disorder with diverse clinical manifestations, often challenging to diagnose accurately. In this study, we leverage wavelet entropy (WE) as a feature extraction method to capture intricate patterns within brain imaging data. These extracted features are then employed to train a neural network model optimized through PSO to enhance classification accuracy. Experimental results on a dataset comprising MS and healthy control subjects demonstrate the effectiveness of our proposed approach, viz., WE-PSONN, which achieves a sensitivity of 91.95±1.15, a specificity of 92.36±0.88, a precision of 92.28±0.88, an accuracy of 92.16±0.90. The combination of advanced signal processing techniques and machine learning optimization holds promise for improving the early diagnosis and management of MS, offering potential benefits to both patients and healthcare providers.

Diabetes is a serious health problem throughout the world, including in Indonesia. The International Diabetes Federation (IDF) reports that the number of adults with diabetes is increasing every year. The Behavioral Risk Factor Surveillance System (BRFSS) is a survey conducted by the Centers for Disease Control and Prevention (CDC) in the United States. Classification methods in data mining techniques are used to classify diabetics and non-diabetics. The data mining process is carried out by preprocessing, feature selection, and dataset classification stages. In the preprocessing stage, data cleaning, data formatting, and data oversampling are carried out using the Synthetic Minority Over-sampling Technique (SMOTE). Next, the feature selection stage is carried out using the Particle Swarm Optimization (PSO) algorithm to find the best attributes. The dataset classification stage is carried out using the CART Model Decision Tree algorithm. The results of the performance evaluation of the CART algorithm are calculated using the confusion matrix and the MAE value, the results obtained for the CART algorithm without SMOTE and PSO obtained the best accuracy of 75.34% and the MAE value of 0.2466, while the CART algorithm using SMOTE and PSO can increase accuracy by 10 .94% to 86.28% and an MAE value of 0.1372.

In current study, the production of microparticles from fifteen commercial sugarcane pulp (SCP) lunch-boxes into different types of food under different conditions was investigated, where we used DI water, 4% HAc, and 95% EtOH to simulate aqueous, acidic, and fatty foods. Results showed that compared with DI water and 95% EtOH, 4% HAc caused the degradation of sugarcane fibers, resulting in the highest number of microparticles released, which was significantly greater than the specified limit value. It was estimated that consumers might intake a total number of 36,400 ~ 231,700 items per meal per lunch-box, of which the proportion of microparticles with particle size between 100 ~ 500 μm was the largest, with a number of 12,376 ~ 130,795 items. Moreover, of all SCP lunch-box except S-11, Al and Fe are the main metals, ranging between 35.16 ~ 1244.04 and 44.71 ~ 398.52 mg/kg, respectively, followed by Pb, Ti, and Sr. Interestingly, unlike other lunch-boxes, the content of Pb in S-11 was the highest, with a number of 136 mg/kg. This study provides important information that the safeness including both the production of microparticles and the metallic elements should be considered for SCP lunch-boxes when in contact with food.

We still do not have satisfactory answers about the inflationary and accelerating expansions of the universe, the missing mass problem, and the origins of the four fundamental forces and the quantum phenomena. Here, we introduce a new model of matter and space and a postulate on a constant of motion, and then show that an equation of motion derived from the constant of motion could provide new understandings of the above problems. For example, we show that there is a natural inflation scenario and that the co-moving distance to the closest accelerating supernova is clearly given as $c/(\sqrt{3} H)$ ($\sim$8.1 billion ly). In addition, we show that, in a system including a proton and an electron, the equation of motion has eight acceleration terms, some of which are suspected to be responsible for the four fundamental forces and the galaxy rotation anomaly. From further discussion, we conjecture that in order to understand the gravitational and strong interactions and the quantum phenomena, it is necessary to consider the expansion of the universe. The lensing mass distribution is explained, based on another postulate on a method of representing superposition.

We consider a new model of a branching random walk on a multidimensional lattice with continuous time and one source of particle reproduction and death, as well as an infinite number of sources in which, in addition to the walk, only absorption of particles can occur. The asymptotic behavior of the integer moments of both the total number of particles and the number of particles at a lattice point is studied depending on the relationship between the model parameters. In the case of the existence of an isolated positive eigenvalue of the evolution operator of the average number of particles, a limit theorem is obtained on the exponential growth of both the total number of particles and the number of particles at a lattice point.

Separation and detection of cells and particles in a suspension are essential for various applications, including biomedical investigations and clinical diagnostics. Microfluidics realizes the miniaturization of analytical devices by controlling the motion of a small volume of fluids in microchannels and microchambers. Accordingly, microfluidic devices have been widely used in particle/ cell manipulation processes. Different microfluidic methods for particle separation include dielectrophoretic, magnetic, optical, acoustic, hydrodynamic, and chemical techniques. Dielectrophoresis (DEP) is a method for manipulating polarizable particles’ trajectories in non-uniform electric fields using unique dielectric characteristics. It provides several advantages for dealing with neutral bioparticles owing to its sensitivity, selectivity, and noninvasive nature. This review provides a detailed study on the signal-based DEP methods that use the applied signal parameters, including frequency, amplitude, phase, and shape for cell/particle separation and manipulation. Rather than employing complex channels or time-consuming fabrication procedures, these methods realize sorting and detecting the cells/particles by modifying the signal parameters while using a simple device. In addition, these methods can significantly impact clinical diagnostics by making low-cost and rapid separation possible.

We report interfacial crystallization in droplets of saline solutions placed on superhydrophobic surfaces and liquid marbles filled with the saline. Evaporation of saline droplets deposited on superhydrophobic surface resulted in the formation of cup-shaped millimeter-scaled residues. The formation of the cup-like deposit is reasonably explained within the framework of the theory of the coffee-stain effect, namely, the rate of heterogeneous crystallization along the contact line of the droplet is many times higher than in the droplet bulk. Crystallization within evaporated saline marbles, coated with lycopodium particles, depends strongly on the evaporation rate. Rapidly evaporated saline marbles yielded dented shells built of a mixture of colloidal particles and NaCl crystals. We relate the formation of these shells to the interfacial crystallization promoted by hydrophobic particles coating the marbles, accompanied with the upward convection flows supplying the saline to the particles, serving as the centers of interfacial crystallization. Convective flows prevail over the diffusion mass transport for the saline marbles heated from below.

With the every passing day, the demand for data traffic is increasing and this demand forces the research community not only to look for alternating spectrum for communication but also urges the radio frequency planners to use the existing spectrum smartly. Cell size is shrinking with the every upcoming communication generation which makes the base station placement planning complex and cumbersome. In order to make the next-generation cost-effective, it is important to design the network in such a way which utilizes minimum number of base stations while ensure coverage and quality of service. This paper aims at develop a new approach using hybrid metaheuristic and metamodel applied in multi-transmitter placement planning (MTPP) problem. We apply radial basis function (RBF) metamodel to assist particle swarm optimizer (PSO) in a constrained simulation-optimization (SO) of MTPP to mitigate the associated computational burden of optimization procedure. We evaluate the effectiveness and applicability of proposed algorithm in a case study by simulating MTPP model with two, three, four and five transmitters.

Particles in classical physics are distinguishable objects, which can be picked out individually on the basis of their unique physical properties. By contrast, in quantum mechanics the standard view is that particles of the same kind (``identical particles'') are completely indistinguishable from each other. This standard view is problematic: Particle indistinguishability is irreconcilable not only with the very meaning of ``particle'' in ordinary language and in classical physical theory, but also with how this term is used in the practice of present-day physics. Moreover, the indistinguishability doctrine prevents a smooth transition from quantum particles to what we normally understand by ``particles'' in the classical limit of quantum mechanics. Elaborating on earlier work, we here discuss an alternative to the standard view that avoids these and similar problems. As it turns out, this alternative approach connects to recent discussions in quantum information theory concerning the question of when identical particles can be considered to be entangled.

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic travelling salesman problem (DTSP). The DTSP can be modelled as a sequence of static sub-problems, each of which is an instance of the TSP. We present a method for automatically setting the values of the DPSO parameters without three parameters, which can be defined based on the size of the problem, the size of the particle swarm, the number of iterations, and the particle neighbourhood size. We show that the diversity of parameter values has a positive effect on the quality of the generated results. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties: it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustement step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection; finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection which RMSE with the reference orthophoto is around two pixels.

The precise prediction of air contaminant dispersion is essential to the air quality monitoring and the emergency management of the contaminant gases leakage incidents in the chemical industry park. The conventional atmospheric dispersion models can seldom give precise prediction due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion model, two data assimilation methods (i.e. one is merely based on the typical particle filter while the other is a combination of particle filter and expectation-maximization algorithm) are proposed to assimilate the UAV observations into the atmospheric dispersion model. Two emission cases are taken into consideration, the difference between which is the different dimensions of state variables. To test the performances of the proposed methods, experiments corresponding to the two emission cases are designed and implemented. The results show that the particle filter can effectively estimate the model parameters and improve the accuracy of model prediction when the dimension of state variables is low. In contrast, when the dimension of state variables becomes higher, the method of particle filter combining expectation-maximization algorithm performs better in the parameter estimation accuracy and warm-up time. Therefore, the data assimilation methods are able to effectively support the air quality monitoring and emergency management in chemical industry parks.

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy.

We present a laser-plasma electron accelerator module designed to be driven by high repetition rate lasers for industrial applications of laser-driven electron beams. It consists of a single vacuum chamber containing all the necessary components for producing, optimizing, and monitoring electron beams generated via laser wakefield acceleration in a gas jet. The core methods involve comprehensive metrological assessments, including rigorous temporal laser pulse characterization and contrast measurements, supplemented by detailed spatio-temporal distribution analyses of the laser system. Results demonstrate the good stability and reproducibility of the laser system, confirming its suitability for advanced scientific and industrial applications. We demonstrate the full functionality of the laser-plasma accelerator module and perform target density and laser-plasma characterizations. Our work is a first step towards the wide dissemination of fully integrated laser-plasma accelerator technology.

Dendrite growth and parasitic reactions with liquid electrolyte are the two key factors that restrict the practical application of the lithium metal anode. Herein, a bis(benzene sulfonyl)imide based single-ion polymer artificial layer for lithium metal anode is successfully constructed, which is prepared via blending the as-prepared copolymer of lithiated 4, 4’-dicarboxyl bis(benzene sulfonyl)imide and 4,4’-diaminodiphenyl ether on the surface of lithium foil. This single-ion polymer artificial layer enables compact structure with unique continuous aggregated Li+ clusters, thus reducing the direct contact between lithium metal and electrolyte, and ensuring Li+ transport fast and homogeneous simultaneously. Based on which, the coulombic efficiency of the Li|Cu half-cell is effectively improved, and the cycle stability of the Li|Li symmetric cell can be prolonged from 160 h to 240 h. Surficial morphology and elemental valence analysis confirm that the bis(benzene sulfonyl)imide based single-ion polymer artificial layer effectively facilitates the Li+ uniform deposition and suppresses parasitic reactions between lithium metal anode and liquid electrolyte in the LFP|Li full-cell. This strategy provides a new perspective to achieve steady lithium metal anode, which can be a promising candidate in practical application.

The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants, the pollution reduction and carbon emissions reduction of coal-fired power plants will be a key task in the future. This article proposes an optimization operation of electrostatic precipitators(ESP), based on the working mechanism and historical data of ESP, voltage-current characteristic model and outlet dust concentration prediction model are constructed by deep learning algorithm, Particle Swarm Optimization(PSO) is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate prediction of secondary current and outlet dust concentration of ESP has been achieved, the Mean Absolute Percentage Error(MAPE) of secondary current is less than 1.5%, and the MAPE of outlet dust concentration of ESP on the test set is less than 5.2%. Finally, the optimization experiment is carried out in a 330MW coal-fired power plants, the results showed that the fluctuation of the outlet dust concentration is more stable, and the energy saving is about 43% after optimization, according to the annual operation of 300 days, the annual average carbon reduction is approximately 2621.34 tons. This method is effective and can be applied widely.

Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond the small pools of subjects implied by “precision medicine”. This manuscript proposes a novel approach for reference standard development to evaluate the accuracy of single-subject analyses of metabolomes, proteomes, or transcriptomes. Since distributional assumptions of statistical testing may inadequately model genome dynamics of gene products, the so-called significant results of previous studies may artefactually conflate with real signals. Model confirmation biases escalate when studies use the same analytical methods in the discovery sets and reference standards, as corroboration of results leads to an evaluation of reproducibility confounded with replicated biases rather than a measure of accuracy. We hypothesized that developing method-agnostic reference standards using effect-size and expression-level filtering of results, obtained from multiple discovery methods that are distinct from the one evaluated, would maximize the evaluation of clinical-transcriptomic signals and minimize statistical artefactual biases. We developed and released an R package “referenceNof1” to facilitate the construction of robust reference standards. Results: Since RNA-Seq data analysis methods often rely on binomial and negative binomial assumptions to non-parametric analyses, the differences create statistical noise and make the reference standards method dependent. In our experimental design, the accuracy of 30 distinct combinations of fold changes (FC) and expression levels (EL) were determined for five types of RNA analyses in two different datasets. This design was applied to two distinct datasets: breast cancer cell lines and a yeast study with isogenic biological replicates in two experimental conditions. In addition, the reference standard (RS) comprised all RNA analytical methods with the exception of the method testing accuracy. To mitigate for biased optimization of the RS parameters towards a specific analytical method, similarity between observed results of distinct analytical methods were calculated across all methods (Jaccard Concordance Index). The greatest differences were observed across diametric extremes. For example, filtering out differentially expressed genes (DEGs) using a fold change < 1.2 leads to a 50% increase in concordance between techniques when compared to results with FC > 1.2. Combining this FC cutoff with genes with mean expressions > 30 counts leads to a 65% increase in concordance in comparison to genes with expression levels < 30 counts and with FC < 1.2. Conclusions: We have demonstrated that comparing accuracies of different single-subject analysis methods for clinical optimization requires a new evaluation framework. Reliable and robust reference standards, independent of the evaluated method, can be obtained under a limited number of parameter combinations: fold change (FC) ranges thresholds, expression level cutoffs, and exclusion of the tested method from the RS development process. When applying anticonservative reference standard frameworks (e.g., using the same method for RS development and for prediction), a majority of the concordant signal between prediction and Gold Standard (GS) cannot be confirmed by other methods, which we conclude as biased results. Statistical tests to determine DEGs from a single-subject study generate many biased results that require subsequent filtering for increasing their reliability. Conventional single-subject studies pertain to one or a few measures in one patient over time [1]and need a substantial conceptual framework extension in order to address the tens of thousands of measures in genome-wide analyses of gene products. The proposed referenceNof1 framework addresses some of the inherent challenges in improving transcriptome scale single-subject analyses by providing a robust approach to constructing reference standards. Github: https://github.com/SamirRachidZaim/referenceNof1

This paper uncovers that quantum uncertain principle makes the single particle with global property have no certain path, and then wave of quantum particle can simultaneously do pass the double slits. The two subwaves after passing Young’s double slits are entanglement, they may form interference of subwaves. Consequently, we find a kind of quantum probabilistic entanglements with Wheeler's delayed choice. Quantum particles such as photons, electrons, neutrons, protons etc mean that wave of the quantum particle can simultaneously do pass through Young's double slits, rather than individual quantum particle may pass through Young's double slits at the same time. When considering wave property, we cannot consider particle property (Just as in the photoelectric effect, considering the particle nature of the system, people cannot consider wave property, otherwise the photoelectric effect cannot appear). Therefore, this paper discovers that the ability of single photon to hit electrons out in photoelectric effect is complementarily equivalent to the ability of wave of the single photon to simultaneously pass through Young's double slits in wave-particle duality. Objective criteria for distinguishing classical and quantum particles are discovered and objectively give the applicable realm of quantum mechanics for the first time. The crisis of the single particle’s simultaneously passing through Young's double slits, which has been plaguing physicists in the whole world up to now for decades, is solved, in which the studies are classified as classical and quantum particles, the classical particle and quantum particle wave cannot and can pass the Young’s slits, respectively. This paper discovers both the new physics mechanism of passing the double slits of the wave with the amplitude of 4-dimensional momentum representation wave function reflecting particle nature and the principle of self-adaptive emergence of wave-particle duality, and then using the principle, this paper gives both direct explanations to the current experiments and new predictions of new some experiments for wave-particle duality. All the deduced results here are consistent with all relevant physics experiments.

Recently, a paper was written to establish semi-structured complex numbers ℍ (a new number set invented to enable division by zero) as a useful mathematical tool providing novel results from its application in engineering and science. Whilst the paper was a milestone, the application of this number set to quantum and classical physics had not been fully explored. Consequently, the aim of this research was to use semi-structured complex numbers to develop a new mathematical model for the mass of an object and in course unite classical and quantum physics in a simple yet effective manner. As its major contributions this paper: (1) develops a new mathematical model for the mass of an object called the wave-mass function using De Broglie’s mass and frequency relation and semi-structured complex numbers; (2) uses the new wave-mass function to determine what happens to an object traveling at the speed of light; (3) derives a relation between Schrödinger’s wave function and the new wave-mass function; (4) derives a relation between Einstein’s Field Equation and the new wave-mass function; (5) uses the wave-mass function to combine Einstein’s Field Equation with Schrödinger’s wave function to form a new quantum gravity equation. These results and their applications provide a firm foundation to advance the number set ℍ as a useful mathematical tool.

A recently proposed single image parasite quantification (SIMPAQ) platform based on a Lab-On-a-Disc (LOD) device has been earlier successfully tested in field conditions demonstrating the efficiency in soil-transmitted helminths (STH) egg detection and analysis. A related study (Micromachines 2021, 12, 1032) revealed the effects that can limit the performance of a SIMPAQ method due to the action of the Euler and Coriolis forces, and the interaction of the moving eggs with the walls of the LOD chamber. Here we propose a new improved design that allows to overcome those limitations and enhance the yield of the SIMPAQ LOD device which is demonstrated in the experiments with synthetic particle model system and real parasite eggs.

Intratumor heterogeneity (ITH) is associated with therapeutic resistance and poor prognosis in cancer patients, and attributed to genetic, epigenetic, and microenvironmental factors. We developed a new computational platform, GATHER, for geostatistical modeling of single cell RNA-seq data to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides new applications of spatial entropy measures for quantification and objective characterization of ITH. It includes new tools for insightful visualization of spatial transcriptomic phenomena. We illustrate the capabilities of GATHER using real data from breast cancer tumor to study hallmarks of cancer in the phenotypic contexts defined by cancer associated fibroblasts.

We report herein high-resolution x-ray diffraction measurements of basal plane bending of homoepitaxial single-crystal diamond (SCD). The results reveal that growth parameters such as temperature, growth time and basal plane bending of the substrate affect the basal plane bending of SCD. First, the basal plane bending of SCD depends mainly on the substrate itself. The basal plane bending of SCD becomes more severe with increasing basal plane bending of the substrate and this type of basal plane bending cannot be recovered. The SCD growth experiments show that the basal plane bending increases at high temperature and with increasing growth time. Finally, to understand the mechanism behind basal plane bending, we investigate the substrate-surface temperature distribution as a function of basal plane bending of SCD fabricated by chemical vapour deposition (CVD). This allows us to propose a bending model and understand the origin of basal plane bending. The results indicate that an uneven temperature distribution on the substrate surface is the main cause of the CVD diamond base-plane bending.

The authors of the article analyze the existing methods and models of technological preparation of machine-building industries. The structure of a three-level simulation model with network-centric control, the structures of individual elements of the simulation model and the process of simulation modeling are described. The criteria for choosing a rational option for the processing technological route have been determined. During the research, a simulation program was implemented in C++. It allows you to select the optimal scenario for the operation of a production site based on two criteria: time and cost. The volume of implementation is about 2x103 lines of code. A diagram of the modeling algorithm for the implemented program, a description of the classes and their interactions are given in the article. The developed simulation model was tested at a machine-building enterprise using the example of the “Pusher” part, manufactured under single-unit production condi-tions. The technological equipment used for the manufacture of this part was formed in the form of input data of the simulation model. The results of simulation modeling for the selected part are described. For each variant of the technological processing route, the values of variable costs and the duration of the production cycle were determined.

Particles and photons appear to be total opposites; the former has rest mass which requires space to exist; the latter has kinetic energy which requires time to occur (oscillate). But they do share certain properties (e.g., quantization) that remain invariant when one is transformed (swapped) for the other. This gauge invariance is developed in some detail.The symmetry between particle and photon turns out to be one of inversion. It is the equalities of special relativity that support this inversion and the accompanying invariances: mass transformed to energy; space transformed to time. The great advantage of these symmetries (inversions) is that they provide guidance for an object little understood (the photon) based upon an object well understood (the particle). On this basis, progress can be made in the understanding of some long-standing issues: wave-particle duality, time’s arrow, the constant speed of light and nonlocality.

Like 18F-FDG, 99mTc-1-thio-D-glucose (99mTc-TG) also binds to GLUT receptors. The aim of this Phase I study was to evaluate the safety, biodistribution and dosimetry of 99mTc-TG. Twelve lym-phoma patients were injected with 729±102 MBq 99mTc-TG. Whole-body planar imaging was per-formed in 10 patients at 2, 4, 6 and 24 h after injection. In all 12 patients SPECT/CT (at 2 h) and SPECT (at 4 and 6 h) imaging was performed. Vital signs and possible side effects were monitored during imaging and up to 7 days after injection. 99mTc-TG injections were well tolerated and no side effects or alterations in blood and urine analyses data were observed. The highest absorbed dose was in the kidneys and urinary bladder wall, followed by the adrenals, prostate, bone mar-row, lungs, myocardium, ovaries, uterus, liver and gall bladder wall. 99mTc-TG SPECT/CT re-vealed foci of high activity uptake in the lymph nodes of all nine patients with known nodal le-sions. Extranodal lesions were detected in all nine cases. In one patient, a lesion in the humerus head, which was not detected by CT, was visualized using 99mTc-TG. Potentially, 99mTc-TG SPECT can be considered as an additional diagnostic method for imaging GLUT receptors in lymphoma patients.

In this research, the concept of the instability of tension in Microgrid still becomes the focus of study and purpose. Scrutinously, First-Swing Instability occurs in the system in noted changes with properties of making the system unstable or restore to a state of stability. The latter calls itself as a phenomenon of re-synchronization. Allied to aspects of system instability, it is evident that by connecting fast electric chargers for battery-electric vehicles to the distribution system from microgrids, they increase the necessary basic electrical charges and reduce the stability of the electrical energy system. Transient electrical currents have the characteristic of severely impacting microgrids not prepared in connected states or island of the main system. The occurrences of the electromagnetic incompatibility of the inrush current, due to their unpredictability and intermittent characteristic, have complex identification studies. With the elevation of electricity demand by battery-electric vehicle chargers, concerns about the effects of the integration of the electricity grid increase proportionally. The research described in this article emphasizes the influence of fast chargers’ initialization on the power variation related to the re-synchronization phenomenon and its mitigation of the stability of electricity microgrid. An optimization algorithm based on particle swarm optimization (PSO) implements the development of virtual impedances. The PSO optimization algorithm analyzes all possible operating points and aims to maximize the stability index of microgrid, maintaining reactive energy incompatibilities at the minimum level supported. The fractional objective function facilitates the service to these goals simultaneously. The optimization algorithm is implemented in a case study and the corresponding virtual impedance is incorporated into the microgrid. Voltage levels are verified as a condition in the optimization process, comparing system effectiveness.

Waste heat recovery stands out as a promising technique to tackle both energy shortages and environmental pollution. Currently, this valuable resource, generated from processes like fuel combustion or chemical reactions, is often dissipated into the environment, despite its potential to significantly contribute to the economy. To harness this untapped potential, a travelling-wave thermo-acoustic generator has been designed and subjected to comprehensive experimental analysis. Fifty-two data corresponding to different working conditions of the system were extracted to build ANN, ANFIS and ANN-PSO models. Evaluation of performance metrics reveals that the ANN-PSO model demonstrates the highest predictive accuracy (R^2=0.9959), particularly in relation to output voltage. This research demonstrates the potential of machine learning techniques for the analysis of thermo-acoustic systems. By doing so, it is possible to get an insight into nonlinearities inherent to thermo-acoustic systems. This advancement empowers researchers to forecast the performance characteristics of alternative configurations with a heightened level of precision.

Sumatriptan is a serotonin receptor agonist approved by FDA in 1992 to treat migraines. Its’ solid form used in commercially available drugs Imitrex, Treximet, Onzetra Xsail and Zembrace symtouch is sumatriptan succinate. Herein we report on crystal structure of sumatriptane succinate hemi(ethanol solvate), HSum+·HSucc–·0.5EtOH, its comparison with previously reported solids containing derivatives of sumatriptan.

Once discovered, cancer stem cells have become a hot topic in the research of cancer therapy. These cells possess stem cell-like self-renewal and differentiation capacities and are important factors that dominate cancer metastasis, therapy- resistance and recurrence. What's worse, their own characteristics make them difficult to be eliminated. Colorectal cancer is the third most common cancer and the second leading cause of cancer death worldwide. Targeting colorectal cancer stem cells (CRCSCs) can inhibit colorectal cancer metastasis, enhance therapeutic efficacy, and reduce recurrence. Here, we introduced the origin, marker proteins, identification, cultivation and research techniques of CRCSCs, summarized the signaling pathways that regulate the stemness of CRCSCs, such as Wnt, JAK/STAT3, Notch, and Hh signaling pathway. In addition to these, we also reviewed anti-CRCSC drugs targeting signaling pathways，markers，and other regulators in recent years. These will help researchers gain insight into the current agents targeting to CRCSCs, explore new cancer drugs, and propose potential therapy.

Along with the economic growth, the companies must contribute to social progress and promote environmental sustainability in equal harmony. Sustainable human resource management (SHRM) strategies make it possible to attain the economic, social and environmental goals of a firm. In this regard, a survey method is discussed using the literature review and online questionnaire to identify the main factors/indicators during the SHRM evaluation of manufacturing firms in India. Uncertainty is commonly occurred in the assessment of SHRM factors. As a generalized version of fuzzy sets, single-valued neutrosophic set (SVNS) has been demonstrated as a valuable tool to illustrate the indeterminate, inconsistent and uncertain data of realistic decision-making problems. Considering the idea of SVNSs, this study develops a hybrid multi-criteria group decision-making (MCGDM) approach for assessing the SHRM of manufacturing firms under uncertainty settings. For this purpose, an SVN-alternative ranking order method accounting for two-step normalization (AROMAN) is proposed based on VIFI-score function-based decision experts’ (DEs’) weighting tool and integrated criteria weight-determining model to solve the MCGDM problems with fully unknown DEs and criteria weights. In this regard, we develop new SVN-distance measure to compute the degree of difference between SVNSs. Some examples are presented to demonstrate the efficacy of developed measure over the existing ones. In addition, new criteria weight-determination model is presented with the integration of objective weights through IVIF-distance measure-based model and subjective weights through ranking comparison (RANCOM) tool on SVNSs. The proposed ranking method is applied to an empirical study of SHRM assessment for manufacturing firms in India, which shows its applicability and feasibility. In this study, the evaluation criteria are characterized into social, environmental and economic aspects with DE’s opinions. Comparative and sensitivity analyses are made to show the strength and steadiness of presented approach. This study provides an innovative MCGDM analysis framework, which makes a significant contribution to the SHRM assessment problem under indeterminate, inconsistent and uncertain setting.

Genetic adaptation of Hermetia illucens (BSF) to suboptimal single sourced wastes can open new perspectives for insect production. Here, four replicate BSF lines were maintained on a single sourced, low-quality wheat bran diet (WB) or on a high-quality chicken feed diet (CF) for 13 generations. We continuously evaluated evolutionary responses in several performance traits to rearing at the two diets. Subsequently, we tested responses to interchanged diets, i.e. of larvae that had been adapted to low-quality feed and to high quality feed and vice versa to evaluate costs associated with diet adaptation. BSF were found to experience rapid adaptation to the diet composition. While performances on WB diet were always inferior to CF diet, the adaptive re-sponses were stronger to the former diet. This stronger response was likely due to stronger se-lection pressure experienced by BSF fed on the low-quality single sourced diet. The interchanged diet experiment found no costs associated with diet adaptation but confirmed diet adaptation for the larval biomass and insect frass traits for the WB diet treatment. Our results revealed that BSF can rapidly adapt to diet through evolutionary responses, which has potential to be utilized to produce lines tailored to specific diets.

Some studies show that patients with mutations in the SNCA gene, which codifies for the alpha-synuclein protein, show a particular phenotype. The effects of SNCA Single Nucleotide Polymorphism (SNPs) in recently diagnosed, drug-naïve patients with PD have been less explored. Therefore, we set out to explore the differences in the clinical characteristics of recently diagnosed drug-naïve sporadic PD patients with or without SNCA rs3910105 or rs356181 SNPs. Patients with a clinical diagnosis of PD in the Parkinson’s Progression Markers Initiative (PPMI) database entered the study. We excluded those with missing data, dementia, psychiatric conditions, a diagnosis change over the first five years from the initial PD diagnosis, or with a familial history of PD. Subjects were evaluated with the MDS-Unified PD Rating Scale (MDS-UPDRS), DAT imaging, the Geriatric Depression Scale (GDS), the State-Trait Anxiety Inventory (STAI), the Montreal Cognitive Assessment (MoCA), the SCOPA-AUT for autonomic function, the Epworth Sleepiness Scale (ESS), the RBD Questionnaire, and the University of Pennsylvania Smell Identification Test (UPSIT). We included 308 PD patients fulfilling all inclusion and exclusion criteria. A logistic regression analysis and Machine-Learning models did not disclose any difference between patients either with or without the SNCA rs3910105 SNP or with or without the SNCA rs3910105 polymorphism. Our results suggest that the SNCA polymorphisms rs3910105 and rs356181 have no impact on the phenotype of idiopathic, sporadic, recently diagnosed, drug naïve PD patients.

This study aimed to describe the injury profiles of young Korean baseball players according to position and age as the proportion and distribution of injuries based on playing position and age remains unclear. A total of 271 elite youth baseball players aged 8 to 16 years were divided into two groups: elementary school (ES) (n=135) and middle school (MS) (n=136). The participants' basic, baseball practice, and injury information were collected. Injuries in the MS group were not limited to the elbow and shoulder, and the injury prevalence varied by age group and baseball position. The most injured body region in the ES group was the elbow joint, regardless of the position. In contrast, the most injured body region in the MS group was the lower back, except for infielders whose elbows were the most injured. Additionally, the MS group was more likely to experience injuries of the lower back (OR=4.27, 95% CI=2.47–7.40), shoulder (OR=1.93, 95% CI=1.08–3.43; P=0.024), and knee (OR=2.15, 95% CI=1.17–3.94; P=0.012). Our findings indicate that excessive practice and lack of rest during MS (growth spurt period) can significantly increase the risk of lower back problems in young baseball players.

Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight GAN-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.

Polyphosphate (polyP) accumulating organisms (PAOs) are the key agent to perform enhanced biological phosphorus removal (EBPR) activity, and intracellular polyP plays a key role in this process. Potential associations between EBPR performance and the polyP structure have been suggested, but are yet to be extensively investigated, mainly due to the lack of established methods for polyP characterization in the EBPR system. In this study, we explored and demonstrated that single-cell Raman spectroscopy (SCRS) can be employed for characterizing intracellular polyPs of PAOs in complex environmental samples such as EBPR systems. The results, for the first time, revealed distinct distribution patterns of polyP length (as Raman peak position) in PAOs in lab-scale EBPR reactors that were dominated with different PAO types, as well as among different full-scale EBPR systems with varying configurations. Furthermore, SCRS revealed distinctive polyP composition/features among PAO phenotypic sub-groups, which are likely associated with phylogenetic and/or phenotypic diversity in EBPR communities, highlighting the possible resolving power of SCRS at the microdiversity level. To validate the observed polyP length variations via SCRS, we also performed and compared bulk polyP length characteristics in EBPR biomass using conventional polyacrylamide gel electrophoresis (PAGE) and solution ³¹P nuclear magnetic resonance (³¹P-NMR) methods. The results are consistent with the SCRS findings and confirmed the variations in the polyP lengths among different EBPR systems. Compared to conventional methods, SCRS exhibited advantages as compared to conventional methods, including the ability to characterize in situ the intracellular polyPs at subcellular resolution in a label-free and non-destructive way, and the capability to capture subtle and detailed biochemical fingerprints of cells for phenotypic classification. SCRS also has recognized limitations in comparison with ³¹P-NMR and PAGE, such as the inability to quantitatively detect the average polyP chain length and its distribution. The results provided initial evidence for the potential of SCRS-enabled polyP characterization as an alternative and complementary microbial community phenotyping method to facilitate the phenotype-function (performance) relationship deduction in EBPR systems.

Rural electrification rate (RER) in Africa is still low to date. Several countries in Sub-Saharan Africa have tried to address this problem using conventional single-phase two-wire or three-phase three-wire systems, however at large costs due to the nature of dispersed rural load centers, low load demand, and low population density. Another solution of off-grid generation creates associated health problems. Therefore, this paper undertakes a review of a single wire earth return (SWER) network as a RER improvement solution. The paper undertakes intensive literature review to elucidate challenges and solutions to the implementation of SWER technology. Advantages of SWER technology discussed make it the choice for RER improvement in Sub-Saharan African countries. After that, a case study is selected in rural Tanzania, and a preliminary SWER network design is undertaken.

This article reveals a best possible design for hybrid dispersion compensating fiber (HyDCF) with high birefringence established on modified broadband compensating structure through S, C and L telecommunication bands. The simulation outcome exhibits relatively higher birefringence of 3.76×10-2 at wavelength of 1550 nm. The suggested fiber also has dispersion compensation characteristics in an inclusive series of wavelengths which covers 1400-1625 nm. The reported design can achieve dispersion quantity of – 606 ps/ (nm.km) at 1550 nm effective wavelength. The reported fiber design matches the relative dispersion slope (RDS) 0.003694 nm-1 similar to single mode fiber at 1550 nm operating wavelength. This fiber demonstrates negatively flattened effective dispersion of – 2.703 ± 0.734 ps/ (nm.km) within 180 nm flat band ranging from 1460-1640 nm wavelength. It is also convenient to optical high bit rate communication systems. The low confinement loss is found 3.756×10-10 dB/m at the operating wavelength. This design also achieves highly nonlinear coefficient of 50.34 W-1km-1. In some cases, it can also be used in sensing applications.