Optimal control is a critical tool for mechanical robotic systems, facilitating precise manipulation of dynamic processes. These processes are described through differential equations governed by a control function, addressing a time-optimal problem with bilinear characteristics. Our study utilizes the classical approach complemented by Pontryagin's Maximum Principle (PMP) to explore this inverse optimal problem. The objective is to develop an exact piecewise control function that effectively manages trajectory control while considering the effects of viscous friction. Our simulations demonstrate that the proposed control law markedly diminishes oscillations induced by boundary conditions. This research not only aims to delineate the reachability set but also strives to determine the minimal time required for the process. The findings include an exact analytical solution for the stated control problem.

The existing PDC(Polycrystalline Diamond Compacts) -cone hybrid bit bearing adopts a unilateral support structure, which is prone to stress concentration in the journal area, resulting in fracture and wear failure of the bearing, thus reducing the service life of the hybrid bit. In this paper, a new type of double supported bearing hybrid bit is proposed. The static strength analysis of unilateral and bilateral support bearing structures is carried out by finite element simulation, and the stress and strain distribution of the two structures under the load of 20KN-100KN is obtained. Experimental devices for unilateral and bilateral support bearing structures are designed and manufactured to complete 50KN-100KN static pressure loading experiments. The results showed that the stress and strain of unilateral and bilateral support bearing increased linearly with the increase of load. Compared with unilateral bearing, when the load was 100KN, the maximum Mises stress of bilateral bearing decreased from 358.80MPa to 211.10MPa, with a decrease of 41.16%. The maximum contact stress decreased from 415.20MPa to 378.10MPa, decreased by 8.94%, and the maximum principal strain decreased from 11.01e-4 to 9.71e-4, decreased by 11.81%. The axial strain in the danger zone was reduced by 14.68% and 17.35%, respectively. It is found that the contact stress of the simulation data is highly correlated with the bearing life, and the service life of the bilateral bearing bit is increased by 8.94%. The simulation data and experimental results provide data support for the production of hybrid bits with bilateral bearing support.

This study employed the Fully Modified Ordinary Least Squares (FMOLS) and the Error Correction Model (ECM) to investigate the long-run and short-run determinants of unemployment rate in Nigeria. To achieve this annual data on unemployment rate, inflation rate, interest rate, exchange rate and population growth from 1981 to 2016 was collected from Central Bank Statistical Bulletins and the World Bank website. The ADF test revealed that the macroeconomic variables are stationary at first difference while the Cointegration test revealed that the variables are cointegrated. Using unemployment rate as dependent variable, the FMOLS model revealed that exchange rate and population growth are positively significantly related to unemployment rate, interest rate and inflation rate were negatively related to unemployment rate but only interest rate was significant. The short run relationship revealed that the coefficient of the ecm(-1) is negative and statistically significant at 5% level indicating that the system corrects its previous period disequilibrium at the speed of 48.93% yearly. This study concludes that high exchange rate and population growth can lead to increase in unemployment rate in Nigeria while the government should develop the industrial sector and non-oil sector in order to generate employment and boost export in Nigeria.

In this letter, it is proposed that cryogenic quantum bits could operate based on the nonlinearity due to the quantum capacitance of two-dimensional Dirac materials, and in particular graphene. The anharmonicity of a typical superconducting quantum bit is calculated and the sensitivity of quantum bit frequency and anharmonicty with respect to temperature are found. Reasonable estimates reveal that a careful fabrication process could reveal the expected properties, thus putting the context of quantum computing hardware into new perspectives.

Digital weight-loss services (DWLSs) combining pharmacotherapy and health coaching have the potential to make a major contribution to the global struggle against obesity. However, there is currently no evidence to counter the widespread concern that these services enable large numbers of unsuitable patients to obtain Glucagon-like peptide-1 receptor agonists (GLP-1 RA). This study retrospectively analysed the rate at which the partner pharmacy network of Australia’s largest DWLS provider committed GLP-1 RA dispensing errors over a 6-month period. The analysis found that 191 (0.14%) of the 135131 dispensed Semaglutide orders contained an error. Non-compliant dose escalation (44.5%) and dispatch errors (32.5%) were the two most common error types. Most errors (89%) were deemed to have been of medium urgency, with 6.8% considered high urgency. A disproportionate number of high urgency cases were from wrong medication (31%) and general complaint errors (15%). A Chi-square test revealed a statistically higher dispensing error rate among orders to female patients (0.16%) compared to males (0.076%). The results lay an important foundation in digital pharmacy literature and suggest that comprehensive DWLSs have the potential to safely collaborate with third-party pharmacies in dispensing GLP-1 RAs.

Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, drill bit condition monitoring techniques have been developed based on wavelet analysis and artificial neural networks (ANN) for automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is varied from 0.2 to 0.6 mm. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions using accelerometer. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The outcome of ANN showed 98% of accuracy in the wear prediction and this results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.

Fungal endophytes have been found in all plants surveyed to date, yet for many fungi the function of endophytism is still unknown. The Foraging Ascomycete Hypothesis (FAH) proposes that saprotrophic fungi utilize an endophytic stage in leaves to modify dispersal. Under this hypothesis, leaves can provide food and water during time of environmental scarcity and they can transport the fungi to other substrates upon dehiscence. If the FAH is accurate, then some endophytes should have the ability to colonize saprobic substrates directly from a leaf-endophyte stage, though this has been little studied. To assess this ability, twelve surface-sterilized leaves of a tropical tree (Nectandra lineatifolia Mez) were placed directly on wood and incubated for six weeks. Fungi from the wood were subsequently cultured and identified by ITS sequences or morphology. 477 fungal isolates comprising 26 OTUs were cultured from the wood, the majority of which belong to saprotrophic genera (70.8% of OTUs, 82.3% of isolates). The mean OTU richness per leaf was 5.67. The term viaphyte (literally, “by way of plant”) is introduced and defined as fungi that colonize living leaves as endophytes and use the leaves to transfer to another substrate, such as wood, when the leaves dehisce. These results strengthen the Foraging Ascomycete Hypothesis and expose the possibility that viaphytism plays a significant role in the dispersal of fungal saprotrophs.

This paper presents a two-stage ADC based on pseudo-differential operational transconductance amplifier (OTA), which is designed for the readout circuit of X-ray linear array sensor. This hybrid ADC employs an incremental sigma-delta ADC and a cyclic ADC, achieving a good trade-off between accuracy and conversion speed. The two stages share the same hardware to reduce power consumption and die area. A common-mood feedback module is used to suppress the influence of charge injection, and the effectiveness is demonstrated by detailed theoretical analysis. A test chip of 14-bit ADC is fabricated in 0.35μm CMOS technology. The measured root mean square (RMS) value of DNL is 0.254 LSB, and the maximum value of INL is -0.776/+1.56 LSB. The measured effective number of bits (ENOB) is 13.43 bits.

The study concentrated on Myzostomida, which are microscopic polychaetes that live in symbiosis with feather stars, within the coastal waters of Hainan Island. Specimens were collected by researchers using SCUBA diving techniques, leading to the identification of a new species, as well as the documentation of four species that had not been previously recorded in Chinese waters. This discovery stands apart from the seven known Myzostomida species found near Hong Kong and the Dapeng Peninsula in Shenzhen. It underscores significant gaps in our comprehension of marine life in the South China Sea, suggesting the existence of unexplored biodiversity within the region.

Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoor and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appears due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes a NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 meters in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated error in multi-floor scene. The proposed method involves two steps: motion pattern recognition and position matching calibration. The motion pattern recognition aims to detect different motion states, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion states and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR , achieving accurate 3D positioning . The average error is reduced from 6.60 m to 1.83 m.

This paper focuses on improving the tracking accuracy for servo systems and increasing the contouring performance of precision machining. The dynamic friction during precision machining is analyzed using the LuGre model. The dynamic and static parameters in the friction model are efficiently and accurately identified using the improved Drosophila Swarm Algorithm based on cross-mutation. The friction tracking error can be deduced by the friction state space and an expression is derived. To compensate for the tracking error caused by friction, a feedforward compensation control is designed to avoid signal lag in traditional friction controllers. Furthermore, the factors of multi-axis parameter mismatching that impact the machining profile accuracy are analyzed for multi-axis control. An adaptive cross-coupled control-based pre-compensation strategy of contour error is designed to reduce both the tracking error and the contour error. The effectiveness of the proposed method is validated through several experiments, which demonstrate a remarkable improvement in tracking performance and contour accuracy.

Information can be considered as the most fundamental, philosophical, physical and mathematical concept originating from the totality by means of physical and mathematical transcendentalism (the counterpart of philosophical transcendentalism). Classical and quantum information, particularly by their units, bit and qubit, correspond and unify the finite and infinite. As classical information is relevant to finite series and sets, as quantum information, to infinite ones. A fundamental joint relativity of the finite and infinite, of the external and internal is to be investigated. The corresponding invariance is able to define physical action and its quantity only on the basis of information and especially: on the relativity of classical and quantum information. The concept of transcendental time, an epoché in relation to the direction of time arrow can be defined. Its correlate is that information invariant to the finite and infinite, therefore unifying both classical and quantum information.

In this paper, we present a method for estimating GPS signals from visual information captured by a monocular camera mounted on a fixed-wing Unmanned Aerial Vehicle. The main challenge in visual odometry from aerial images is the computation of scale due to irregularities in the elevation of the terrain. That is, it is not possible to accurately convert from pixels in the image to meters in space, and the error accumulates. The contribution of this work is to reduce the accumulated error by comparing the images from the camera with satellite images. The algorithm has been tested in real-time experiments, and the results show that it is possible to eliminate the accumulated error using satellite images.

The present study deals with the transplantation of mature Argan trees (Argania spinosa L. Skeels) and focuses on optimizing survival rates depending on the seasons, the type of shoot pruning applied to the trees and preconditioning treatments consisting of root pruningand watering for a few months before transplanting. So far, the transplantation of this species at the adult stage has not received enough attention in spite of its ecological and socio-economic importance for the regions where it thrives. The results obtained show that transplanting in summer, autumn and winter resulted in survival rates exceeding 50%. The Argan trees transplanted in spring hardly achieved 10% survival. However, the total cutback of the crown induced survival improvement that reached a rate greater than 70% for summer, autumn and winter, and 50% for spring. Watering alone or combined with root pruning for 6 months helped improve the recovery rate but only when carried out in autumn with transplantation in spring. On the other hand, when these pre-treatments were applied in the spring to transplant the trees in summer, the recovery rate was not improved.

In this paper, a new approach is proposed for designing ultra-low-power FFT (Fast Fourier Transform) system suitable for use in energy harvesting powered sensors. Bit-serial architecture is adopted to reduce the power consumption of butterfly operation. Simulation results show that, compared with state-of-the-art bit-serial and conventional parallel processors, the proposed technique is superior in terms of silicon area, power consumption, dynamic energy use due to variable precision arithmetic. A sample design of a 64-point FFT shows that the implementation can save about 40% area and 36% leakage power compared with a conventional parallel counterpart, accordingly achieving significant power benefits at a low sample rate and low voltage domain. The dynamic variation of the arithmetic precision can be achieved through a simple modification of the controller with hardware area overhead of 10% gate count.

This paper presents the analysis of four error models that can appear in the field of adaptive systems, where adaptive laws for parameter estimation are represented as fractional order differential equations whose order can switch between an order within the range (0,1) and 1. These switched adaptive laws have been proposed in the past few years to improve the balance between control energy and system performance, which is often a challenge when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. Boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case.

Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on eight subjects while performing 5-minute sitting and walking tasks. Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.

A dynamic impact test was performed on as-rolled AZ31 alloy with gradient microstructure under various strains. The microstructural evolution and mechanical properties were systematically investigated. As the strain rate gradually increases, an increasing number of twins are formed, facilitating dynamic recrystallization (DRX), and the mechanical properties are also gradually improved as well. The microstructure becomes heterogeneous at higher strain rates, but the peak stress decreases. The impact process results in significantly higher performance due to microstructural refinement, work hardening by dislocations and precipitates. In addition, both the adiabatic shear band and the adjacent crack experience a temperature rise that exceeds the recrystallization temperature of the alloy. This observation also explains the presence of ultrafine recrystallized grains within the adiabatic shear band and the appearance of molten metal around the crack.

Steganography is the branch of cryptology that deals with concealing information inside another information. it’s prefer over cryptography as it hides a message inside another innocent looking message, while cryptography shows scrambled message that might arouse attention of an attacker, however the challenges in steganography is the modification of carrier that causes some abnormalities which is detectable and often the methods are not optimize. This paper presents a novel approach in cryptography and steganography, which utilizes mathematical concept of bits combination to optimize time of transmission using sets of multiple transmitter, and receiver’s addresses where each abstractly represents a bits combination without itself modified. In addition, the paper introduces a concept of dynamical clockwise and anti-clockwise rotation of bits combination over addresses after every transmission to reinforce resistivity against steganalysis.

In the field of delta-sigma modulators, reducing system power consumption without sacrificing accuracy has become a challenge. The summing circuit, as the main part of the cascade-of-integrator-in-feedforward delta-sigma modulator, consumes a significant amount of power in active summing. On the other hand, passive summing circuits have low power consumption but can cause attenuation in the summing results. This article proposes a low-power multi-bit delta-sigma modulator based on passive and attenuationless summation scheme. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through bidirectional sampling technique, compensating for the inherent attenuation caused by passive summation, thus eliminating the need for active OTA to achieve perfect summation. A 2-order delta-sigma modulator based on 4-bit 1-order passive noise-shaping (NS) SAR quantizer is designed to verify the scheme. By combining a passive NS SAR as the 4-bit quantizer and the third-stage integrator of the system, and incorporating an energy-efficient cascode floating inverter amplifier (FIA) structure as the core active OTA, these elements have significantly enhanced the energy-efficiency of the modulator. Simulation results show that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and Schreier figure-of-merit (FoM) of SNDR is 183.92 dB.

Autosomal dominant polycystic kidney disease (ADPKD) is an inherited disorder characterized by renal tubular cystic dilatations. The cysts can develop anywhere along the nephron and over time the cystic dilatation lead to kidney enlargement. On the other hand, the cysts begin to reduce the number of functional nephrons as a consequence of cystic expansion that further contributes to the decline in renal function over the years. The pressure exerted by the dilated cysts lead to compensatory mechanisms that further contribute to the decline in renal function. These structural changes are responsible of glomerular hyperfiltration states, albuminuria, proteinuria, and haematuria. However, the presentation of ADPKD varies in children from a completely asymptomatic child with incidental ultrasound detection of cysts to a rapidly progressive disease. There have been reports of early onset of ADPKD in children younger than 2 years that showed a more rapid decline in renal function. ADPKD is caused by mutation in PKD1 and PKD2 genes. Today, the PKD1 gene mutation seems to account for up to 85% of the cases worldwide, and it is associated with worse renal outcomes. Individuals with PKD2 gene mutation seem to present a milder form of the disease, with a more delayed onset of end stage kidney disease. The cardinal sign of ADPKD is the presence of renal cysts during renal ultrasound. The current guidelines provide the clinicians the recommendations for genetic testing in children with a positive family history. Given that the vast majority of children with ADPKD present with normal or supra normal kidney function we explored the glomerular filtration rates dynamics and the renal ultrasound adjusted percentiles. 14 out of 16 patients had kidney percentile over 90%. The gene mutations were equally distributed among our cohort. In addition, we compared the modified Schwartz formula to the quadratic equation after adjusting the serum creatinine measurements. It seems that even though children with ADPKD have enlarged kidneys, the renal function is more likely normal or near normal when the quadratic estimation of glomerular filtration rate is used (qGFR tended to be lower, 111.95 ± 12.43 ml/min/1.73m2 compared to Schwartz eGFR 126.28 ± 33.07ml/min/1.73m2, p=0.14). Also, when the quadratic equation was employed, not even a patient reached the glomerular hyperfiltration thresholds. The quadratic formula showed that glomerular filtration rates are linear or slightly decreasing after 1-year of follow-up (Quadratic ΔeGFR=0.32±5.78ml/min/1.73m2) as opposed to Schwartz formula that can falsely classify children in a hyperfiltration state ΔeGFR=7.51±19.46ml/min/1.73m2), p=0.019.

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

The largest part of the image encryption algorithms operates on the pixel as a central element by implementing diffusion confusion and eventually a permutation. On the other side, a permutation applied at the bit level changes not only the pixel value, but also its location within the image. In this work, we will propose a new technology of medical and color image encryption, based on chaotic permutations acting at the bit level, and a diffusion confusion ensured by an application of the method of Vigenere largely improved and adapted to the subject. Simulations performed on a large number of images of different sizes and formats ensure that our method is not subject to any known attacks.

Post-training quantization is pivotal in deploying convolutional neural networks for mobile applications. Block-wise reconstruction with adaptive rounding, as employed in prior works like BrecQ and Qdrop, facilitates acceptable 4-bit quantization accuracy. However, adaptive rounding is time-intensive, and its constraint on weight optimization space curtails the potential for quantization performance. The optimality of block-wise reconstruction hinges on the quantization status of subsequent network blocks. In this investigation, we delve into the theoretical underpinnings of the limitations inherent in adaptive rounding and block-wise reconstruction. Our exploration leads to the development of a post-training quantization methodology, designated as AE-Qdrop. This algorithm operates in two distinct phases: block-wise reconstruction and global fine-tuning. The block-wise reconstruction phase introduces a progressive optimization strategy, superseding adaptive rounding, which not only augments quantization precision but also significantly improves quantization efficiency. To mitigate the risk of overfitting, we introduce a random weighted quantized activation mechanism. During the global fine-tuning phase, we account for interdependencies among quantized network blocks. The weight of each network block will be corrected with logit matching and feature matching. Extensive experiments validate that AE-Qdrop achieves high-precision and efficient quantization. For instance, in the case of 2-bit MobileNetV2, AE-Qdrop outperforms Qdrop by achieving a 6.26% enhancement in quantization accuracy and quintupling the quantization efficiency.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

As one of the fastest-growing new energy sources, wind power technology has attracted widespread attention from all over the world. In order to improve the quality of wind power generation, wind speed prediction is an indispensable task. In this paper, an error correction based Variational Mode Decomposition and Broad Learning System (VMD-BLS) hybrid model is proposed for wind speed prediction. First, the wind speed is decomposed into multiple components by the VMD algorithm, and then an ARMA model is established for each component to find the optimal number of sequence divisions. Second, the BLS model is used to predict each component, and the prediction results are summed to obtain the wind speed forecast value. However, in some traditional methods, there is always time lag, which will reduce the forecast accuracy. To deal with this, a novel error correction technique is developed by utilizing BLS. Through verification experiment with actual data, it proves that the proposed method can reduce the phenomenon of prediction lag, and can achieve higher prediction accuracy than traditional approaches, which shows our method’s effectiveness in practice.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

Melioidosis is an endemic infectious disease caused by Burkholderia pseudomallei bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts, with each exhibiting varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran’s I, and the relationship between melioidosis morbidity rate and the environmental indicators was evaluated using geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations.

The extensive use of polypropylene (PP) in various industries has heightened interest in devel-oping efficient methods for recycling and optimising its mixtures. This study focuses on formu-lating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The in-vestigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, at-taining R² values over 0.99. Symbolic regression further enhanced these models, offering signif-icant improvements. To mitigate overfitting, empirical noise and variable swapping were in-troduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the de-velopment of recycling technologies and sustainable practices in the polymer industry by opti-mising processing and enhancing the use of recycled materials.

A construction for the systems of natural and real numbers is presented in Zermelo-Fraenkel Set Thoery, that allows for simple proofs of the properties of these systems, and practical and mathematical applications. A practical application is discussed, in the form of a Simple and Linear Fast Adder (Patent Pending). Applications to finite group theory and analysis are also presented. A method is illustrated for finding the automorphisms of any finite group $G$, which consists of defining a canonical block form for finite groups. Examples are given, to illustrate the procedure for finding all groups of $n$ elements along with their automorphisms. The canonical block form of the symmetry group $\Delta_4$ is provided along with its automorphisms. The construction of natural numbers is naturally generalized to provide a simple and sound construction of the continuum with order and addition properties, and where a real number is an infinite set of natural numbers. A basic outline of analysis is proposed with a fast derivative algorithm. Under this representation, a countable sequence of real numbers is represented by a single real number. Furthermore, an infinite $\infty\times\infty$ real-valued matrix is represented with a single real number. A real function is represented by a set of real numbers, and a countable sequence of real functions is also represented by a set of real numbers. In general, mathematical objects can be represented using the smallest possible data type and these representations are calculable. In the last section, mathematical objects of all types are well assigned to tree structures in a proposed type hierarchy.

Using geometric considerations, we provide a clear derivation of the integral representation for the error function, known as the Craig formula. We calculate the corresponding power series expansion and prove the convergence. The same geometric means finally help to systematically derive handy formulas that approximate the inverse error function. Our approach can be used for applications in e.g. high-speed Monte Carlo simulations where this function is used extensively.

Introduction: While preparing for dive exams, divers are trained to perform rescue exercises. Currently, divers think they will manage and control their body at any time.Methods: At the bottom of the water, the student takes the instructor who simulates a sickness, up to the surface, by controlling the going up. Then, if the exercise is wrong, both divers go down and exercise again. If the exercise is successful, both divers go down to perform another exercise until they are tired or until they have no more air. Results: I discovered that there was a mistake in the preparation of divers for rescue training due to Descartes’ error, “I think, thus I am”, which is part of our collective memory. Going up and going down after a sickness simulation is a repetitive action of the body and going down is therefore part of the automatisms of rescue, which is very dangerous. Conclusions: The body is not necessarily controlled by the present thinking, and if divers forget sickness while going up in a real rescue situation, they will go down automatically because their bodies were trained like this. In addition, it is very easy to forget sickness. Divers should not go down after rescue, they should be on the dive boat or on the land. Descartes’ error has to be reversed: “I am, thus I think” is right. The divers’ rescue exercises must be simplified and divers should never go down after the simulation of a sickness.

COVID-19 outbreak started in the Chinese city of Wuhan and spread around the globe in months due to its high contagious level. Hence, this disease posed a great challenge to researchers and mathematicians. Numerous mathematical models have been suggested to visualize the spreading speed and trends of this pandemic. In this paper, a comparison of a widely established and accepted method, the Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model with a newly-proposed fractional-order SEIQRDP is studied. Densely populated Countries of Asia (Pakistan, India, and Bangladesh) have been chosen as data sets and both algorithms have been applied to their data. The same comparison technique has also been used on the data of two polar countries, New Zealand and Russia, that validated our findings. Error comparison of both algorithms has been recorded in a tabulated form which shows multi-fold erroneous trends over WHO data of selected countries.

In this paper, it is proved that s-iteration with error and Picard-Mann iteration with error iterative processes converge strongly to the unique fixed point of Lipschitzian strongly pseudo-contractive map. This convergence is almost -stable and -stable. Applications of these results to the operator equations, where is a strongly accretive and accretive mappings of into itself.

Chaotic theory is commonly being researched for application in image encryption scheme (IES). In this paper, a novel two-dimensional cross hyperchaotic Sine-modulation-Logistic map (2D-CHSLM) based on the famous Sine and Logistic maps. The hyperchaotic behaviors of 2D-CHSLM is testified with the help of their bifurcation diagrams, trajectory plots, Lyapunov exponents, sample entropy, C0 complexity, permutation entropy and 0-1 test. A new bit-level IES with traditional encryption structure is proposed utilizing 2D-CHSLM. The 2D-CHSLM-IES performs a 2D-CHSLM-based bit-level confusion using zig-zag transform and a 2D-CHSLM-based cross coupled diffusion operations for generating a highly secure cipher-image. Then the simulations and security analyses are further carried out with the help of key space, key sensitivity, histogram, correlation, information entropy, differential attack, noise and data loss attacks, prove that the security of the proposed 2D-CHSLM-IES

The cutter structure and layout scheme of PDC (Polycrystalline Diamond Compact) bits are important factors in improving efficiency. To further improve the drilling efficiency of PDC bits, axe, triangular prism, and cylindrical PDC cutters were used as research objects. Based on the measured granite data, a finite element model of non-homogeneous granite was established and verified by uniaxial compression simulation. The rock-breaking process of different cutter combination schemes was compared using the finite element method, and the parameters in the best scheme were optimized using the Box-Behnken response surface method. The results show that the constructed model of non-homogeneous granite is consistent with the stress-strain relationship of real granite and is reliable. The axe PDC cutter is more aggressive than the other two cutters and is more suitable for the front row of the bit blade tooth arrangement, while the triangular prism cutter is the second most aggressive and is suitable for the rear row of tooth arrangement, and the best combination scheme is the front row of axe cutter and the rear row of triangular prism cutter arranged alternately with the axe cutter. The optimal transverse and longitudinal distances of the optimized triangular prism cutter from the front axe cutter are 10mm and 7mm, and the optimal transverse and longitudinal distances of the rear axe cutter from the front cutter are 10.06mm and 7mm. The drilling speed is more stable during drilling and the PDC bit with mixed tooth arrangement has 16.8% and 16.6% higher rate of penetration(ROP) compared with the bit with single axe cutter and triangular prism cutter, and the drilling speed is more stable during working, which can effectively improve the rock breaking efficiency of the PDC bit. The field application proves that the bit with mixed cutter arrangement is easier to break the complex formations, with more stable ROP and better efficiency. The study can provide theoretical support for the cutter layout of the PDC bit.

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

In this work, satellite data from the Clouds and Earth’s Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments is analyzed to determine how the global absorbed sunlight and global entropy generation rate have changed during the time period from 2002 to 2023. The data is used to test hypotheses derived from the Maximum Power Principle (MPP) and Maximum Entropy Production Principle (MEP) about the evolution of earth’s surface and atmosphere. The results indicate that the amount of absorbed sunlight has increased over the last 20 years but rising surface temperatures have caused the entropy generation rate to remain approximately constant despite that decrease in albedo. The results are simpler to explain using the MPP but do not completely rule out the MEP. Given the acceptance of the MPP or MEP, some extensions and nuances are discussed.

Background: Anxiety disorders can be both an independent risk factor of many somatic conditions and the cause of complications of various organic disorders leading to poor prognosis. Objectives: The purpose of this study is to compare the heart rate variability (HRV) recorded both during the day and at night in patients with anxiety disorders and healthy individuals. Methods: The study was conducted on fifty consecutive outpatients with anxiety disorders who participated in intensive group psychotherapy. The diagnosis was made according to DSM-IV-TR criteria using the PSE-10 questionnaire. Out of 50 patients under study, 17 were diagnosed with panic disorder (PD) and 21 with generalized anxiety disorder (GAD). The control group consisted of 40 healthy people. The resulting three groups were compared in terms of circadian HRV using the Oxford Medilog Suprima Holter System. Results: The HRV analysis showed the nocturnal falls in the time and frequency parameters related to parasympathetic activity (rMSSD, pNN50, HF). The most unfavourable alterations were observed in the PD group, which comprised the disappearance of day vs. night amplitude of the majority of HRV parameters. Hence, the PD group was particularly susceptible to the arrhythmic events. It was noticeable that nocturnal vagotonia in the PD group was significantly lower as compared with the GAD and control group. Conclusions: The innovative aspect of the present study was to find a distinct profile of HRV with regard to the types of anxiety disorders because of the existing considerable differences between the nature of PD and GAD.

Smartwatches are one of the most relevant fitness trends of the past two decades, and they collect increasing amounts of health and movement data. The accuracy of these data may be questionable and requires further investigation. Therefore, the aim of the present study is to validate smartwatches for use in triathlon training. Ten different smartwatches were tested for accuracy in measuring heart rate, distance (via global navigation satellite systems, GNSS), swim stroke rates and the number of swim laps in a 50 m Olympic-size pool. The optical heart rate measurement function of each smartwatch was compared to that of a chest strap. Thirty participants (15 females, 15 males) ran five 3-minute intervals on a motorised treadmill to evaluate the accuracy of the heart rate measurements. Moreover, for each smartwatch, running und cycling distance tracking was tested over six runs of 4,000 m on a 400 m tartan stadium track, six hilly outdoor runs over 3.4 km, and four repetitions of a 36.8 km road bike course, respectively. Three swimming protocols ranging from 200 m to 400 m were performed in triplicate in a 50 m Olympic-size pool, evaluating tracked distance and detected number of strokes. The mean absolute percentage errors (MAPEs) for the average heart rate measurements varied between 3.1% and 8.3%, with the coefficient of determination ranging from 0.22 to 0.79. MAPE results ranged from 0.8% to 12.1% for the 4,000 m run on the 400 m track, from 0.2% to 7.5% for the 3.4 km outdoor run, and from 0.0% to 4.2% for the 36.8 km bike ride. For the swimming tests, in contrast, the deviations from the true distance varied greatly, starting at a 0.0% MAPE for the 400 m freestyle and reaching 91.7% for the 200 m medley with style changes every 25 m. In summary, for some of the smartwatches, the measurement results deviated substantially from the true values. Measurements taken while road cycling over longer distances with only a few curves were in relative terms more accurate than for outdoor runs and even more accurate than for the 400 m track. In the swimming exercises, the accuracy of the measured distances was severely deteriorated by the medley changes among the majority of the smartwatches. Altogether, the results of this study should help in assessing the accuracy and thus suitability of smartwatches for general triathlon training.

Elevated immunoglobulin E (IgE) levels are commonly associated with allergies. However, high IgE levels are also found in several other infectious and non-infectious disorders. Elevated IgE levels typically suggest allergies, eczema, or recurrent skin infections. Very high IgE levels are uncommon in patients with active eczema but typically reflect a monogenic atopic condition or inborn immune defects with an atopic phenotype. The aim of our research is to identify if the possible cause of high IgE levels is also dependent on external factors, age, or associated recurrent pathologies such as infections. To answer this question, we performed a one-year retrospective study that included 200 hospitalized children who had at least two viral or bacterial infections in the six months preceding hospitalization. Measurements of IgE and allergen panels (respiratory and digestive) using blood samples revealed that individuals who tested positive for the body’s synthesis of hyperimmunoglobulin E were not observably allergic to any potential allergens despite having higher total serum IgE. According to the results, there was a strong correlation between elevated IgE serum levels and a history of infectious diseases among the patients.

The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control tools. In statistical process control, control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. In the control chart, the mean value is taken as the center line, and 3σ limits are placed on either side of the center line. In CPV, manufactured lots are summarized, trended, and discussed at a cross-functional level at pre-defined intervals (either weekly, bi-weekly, or even monthly) based on the batch run rate. A cross-functional team assesses details regarding 1) violation frequency, 2) statistical strength of the signal, 3) violation magnitude and its impact on process or quality, and 4) any potential root causes for out-of-expectation or out-of-trend signals. The cross-functional team must analyze whether any signal identified by the control chart is reliable and must determine whether corrective action should be taken and justify the decision in the CPV meeting minutes based on statistical strength and reliability of the signal. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high-precision seawater refractive index measurements. A seawater refractive index measurement system based on position-sensitive detectors is an effective means to achieve high-precision measurement of seawater refractive index. However, in the actual seawater refractive index measurement process, the impact of the measurement error caused by the jitter signal obtained from the measurement on the measurement results cannot be ignored. In this paper, we theoretically analyse the causes of the long jitter signals during seawater refractive index measurement and quantify the influencing factors. Through the analysis, it can be seen that the angle between the two windows in the refractive index measurement area of seawater causes a great error on the measurement results, so the angle between the two windows should be controlled within 2.06° in order to effectively reduce the measurement error. At the same time, the factors affecting the measurement accuracy of the position-sensitive detector are analysed, and the factors leading to the reduction of the measurement accuracy of the position-sensitive detector are mainly the background light, the size of the photosensitive surface, and the change of the temperature of the working environment. For the above factors, we add a narrow-band filter with a bandwidth of 0.9nm in front of the photosensitive surface of the position-sensitive detector during the construction of the measurement system, so as to filter out stray light other than the signal light source. On the premise of ensuring the measuring range of the refractive index of seawater, a position-sensitive detector with a photosensitive surface size of 4 mm×4 mm was selected. In order to reduce the temperature variation of the working environment of the position-sensitive detector, the measurement system was partitioned. In order to validate the testing error range of the optimised test system, standard seawater samples under the same conditions were measured. It can be seen that the jitter signal of the measurement system has been reduced from 0.0022mm before the optimisation to 0.0011mm after the optimisation, and the deviation of the refractive index has been reduced by 1.09×10−8RIU. The experimental results show that the refractive index of seawater has been effectively reduced by adjusting the optical path and the structure of the seawater refractive index measurement system.

The composition of fine-ground lead zirconate-titanate powder Pb(Zr_0.52Ti_0.48)O₃, suspended in PZT and bismuth titanate (BiT) solutions, is deposited on the curved surface of IN718 and IN738 nickel-based supper alloy substrates up to 100 µm thickness. Photochemical metal organic and infiltration techniques are implemented to produce smooth, semi-dense, and crack-free random orientated thick piezoelectric films as piezo-sensors, free of any dopants or thickening polymers. Every single layer of the deposited films is heated at 200 °C with 10 wt.% excess PbO, irradiated by UV lamp (365 nm, 6 watt) for 10 minutes, pyrolyzed at 400 °C, and subsequently annealed at 700 °C for one hour. This process is repeated successively until reaching the desired thickness. Au and Pt thin films are deposited as the bottom and top electrodes using evaporation and sputtering methods, respectively. PZT/PZT and PZT/BiT composite films are then characterized and compared to similar PZT and BiT thick films deposited on the similar substrates. The effect of composition and deposition process is also investigated on the crystalline phase development and microstructure morphology as well as dielectric, ferroelectric and piezoelectric properties of piezo-films. The maximum remnant polarization of P_r = 22.37 ± 0.01, 30.01 ± 0.01 µC/cm², the permittivity of ε_r = 298 ± 3, 566 ± 5 and piezoelectric charge coefficient of d₃₃ = 126, 148 m/V were measured versus the minimum coercive field of E_c = 50, 20 kV/cm for the PZT/PZT and PZT/Bit thick films, respectively. The thick film piezo-sensors are developed to be potentially used at frequency bandwidth of 1–5 MHz for rotary structural health monitoring and also in other industrial or medical applications as a transceiver.

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network’s sustainable operation and growth. In this paper, we consider a WPT-MEC network wit user cooperation to migrate the double near-far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Rigorous mathematical analysis shows that our algorithm achieves O(1/V),O(V) trade-off between computation rate and queues stability. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability.

The main question proposed in this research is if different types of organizational approach, adapted from rapidly changing disciplines — such as IT industry, and software engineering in particular — could help overcome this gap by shortening the response time needed to analyze advanced solutions in accordance with changed circumstances, thus allowing for architectural programming to obtain different positions in the timeline of project planning and realization. We propose a novel methodology of architectural design as a self-reliant instrument inspired by Agile Manifesto and some of its instantiations, most notably by the Scrum Framework.

Wi-Fi fingerprinting positioning systems have been deployed for a long time in location-based services for indoor environments. Combining mobile crowdsensing and Wi-Fi fingerprinting systems could reduce the high cost for collecting the necessary data enabling the deployment of the resulting system for outdoor positioning in areas with dense Wi-Fi coverage. In this paper, we present the results attained in the designing and evaluation of an urban fingerprinting positioning system based on crowdsensed Wi-Fi measurements. We first asses the quality of the collected measurements, highlighting the influence of received signal strength on data collection. We then, evaluate the proposed system by comparing the influence of the crowdsensed fingerprints on the overall positioning accuracy for different scenarios. The evaluation helps gain valuable insight into the design and deployment of urban Wi-Fi positioning systems while also allowing the proposed system to match GPS-like accuracy in similar conditions.

One of the factors in the mobility of a nation's economy is the stock exchange which plays a decisive role in sustainable economic development. Accordingly, and for the special place of the stock exchange in the economy, we decided to investigate the relationship between the interest and exchange rates and the Tehran stock exchange dividend and price index (TEDPIX). Using the autoregressive distributed lag method, the present study is conducted utilizing the annual data over the period of 1991 to 2020. The results indicate that the exchange rate has a positive long-run relationship with the TEDPIX, but no statistically significant relationship was found between the TEDPIX and the interest rate. Furthermore, the results of the estimation of the error correction model show that 30.786% of the disequilibrium in each period is adjusted to the long-run equilibrium.

All sensing systems have some inherent error. Often, these errors are systematic, and observations taken within a similar region of space and time can have correlated error structure. However, the data from these systems are frequently assumed to have completely independent and uncorrelated error. This work introduces a correlated error model for GNSS-reflectometry (GNSS-R) using observations from NASA’s Cyclone Global Navigation Satellite System (CYGNSS). We validate our model against near-simultaneous observations between two CYGNSS satellites, and double difference our results with modeled observables to extract the correlated error structure due to the observing system itself. Our results are useful to catalogue for future GNSS-R missions and can be applied to construct an error covariance matrix for weather data assimilation.

In this paper, we shall introduce the interval-valued spatial error model. Based on the idea of least square method of single-valued case, we give the parameter estimator for interval-valued spatial error model. The theoretical properties of the proposed estimator are proved. Finally, we give the numerical analysis and a real example.

Purpose: To compare refractive prediction error (PE)s between phacotrabeculectomy and phacoemulsification. Methods: Refractive PE was defined as the difference of spherical equivalent between the predicted value using the Barrett Universal II formula and the actual value obtained at postoperative one month. Forty-eight eyes that had undergone phacotrabeculectomy (19 eyes, open-angle glaucoma; 29 eyes, angle-closure glaucoma) were matched with 48 eyes that had undergone phacoemulsification by age, average keratometry value and axial length (AL), and their PEs were compared. The factors associated with PE were analyzed by multivariable regression analyses. Results: Phacotrabeculectomy group showed a larger absolute PE compared to the phacoemulsification group (0.51 ± 0.37 Diopters vs. 0.38 ± 0.22, P=0.033). Larger absolute PE was associated with longer AL (P=0.010) and higher intraocular pressure (IOP) difference (P=0.012). Hyperopic shift (PE>0) was associated with shallower preoperative anterior chamber depth (ACD) (P=0.024) and larger IOP difference (P=0.031). In the phacotrabeculectomy group, the PE was inversely correlated with AL: long eyes showed myopic shift and short eyes hyperopic shift (P=0.002). Conclusions: Surgeons should be aware of the possibility of worse refractive outcomes when planning phacotrabeculectomy especially in eyes with high preoperative IOP, shallow ACD, and/or extreme AL.

High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained increasing attention due to the risk of drawing inaccurate conclusions if measurement errors are ignored. When the dimension p is larger than the sample size n, it is challenging to develop statistical inference methods for high-dimensional measurement error regression models due to the existence of bias, nonconvexity of objective function, high computational cost and many other difficulties. Over the past few years, some works have overcome the aforementioned difficulties and proposed several statistical inference methods. This paper mainly reviews the current development on estimation, hypothesis testing and variable screening methods for high-dimensional measurement error regression models and shows the theoretical results of these methods with some directions worthy of exploring for future research.

A method of accuracy error compensation for the time interval is examined, allowing to eliminate the dependence of accuracy error on the duration of measured intervals and minimising the influence of destabilising factors - ambient temperature changes and the initial deviation of the meter clock generator frequency from the nominal value. The compensation method is based on a calibration procedure that measures precise time intervals under conditions of changing ambient temperature. Then the dependence of the accuracy error on temperature for a particular meter is recorded. Based on these data, a correction table is compiled containing correction factors and temperature values at which these factors were determined. Under real measurement conditions, the correction factor corresponding to the current temperature is determined from the table for measured results correction. The table with correction factor values could be stored in the memory of the meter or processing computer. Experimental verification of the method showed that applying a correction for a meter with a standard XO class clock generator (certificated instability of ±50 ppm) can obtain an equivalent clock generator instability of ±0.15 ppm. The application of the method is efficient in cases where the use of high-end clocking to ensure the required measurement accuracy is not economically feasible.

Real-world time series data sets contain a combination of linear and nonlinear patterns, making the time series forecasting problem more challenging. In this paper, a new hybrid methodology is introduced for forecasting univariate time series data sets using a multiplicative error modeling approach. An autoregressive integrated moving average (ARIMA) model is combined with an autoregressive neural network (ARNN) for improving the predictions of individual forecast models. The proposed multiplicative ARIMA-ARNN model glorifies the chances of capturing the different combinations of linear and nonlinear patterns in time series. The model shows outstanding performance on six standard time-series data sets compared to other widely used single and hybrid forecasting models.

Few studies have examined the dietary protein intake and sources in combination with the longitudinal changes in brain structure markers. Our study aimed to examine the association between dietary protein intake and different sources of dietary protein with the longitudinal rate of change in brain structural markers. A total of 2723 and 2679 participants from the UK Biobank were included in the analysis. The relative and absolute amounts of dietary protein intake were calculated through a 24-hour dietary recall questionnaire. The longitudinal change rates of brain structural biomarkers were computed using two waves of brain imaging data. We utilized multiple linear regression to examine the association between dietary protein and different sources and the longitudinal changes in brain structural biomarkers. Restrictive cubic splines were used to explore nonlinear relationships, and stratified and sensitivity analyses were conducted. Increasing the proportion of animal protein in dietary protein intake is associated with a slower reduction in the total hippocampus volume (THV, β: -0.02524, P<0.05), left hippocampus volume (LHV, β: -0.02435, P<0.01) and right hippocampus volume (RHV, β: -0.02544, P<0.05).Higher intake of animal protein relative to plant protein is linked to a lower atrophy rate in THV (β: -0.01249, P<0.05) and LHV (β: -0.01173, P<0.05) and RHV (β: -0.01193, P<0.05). Individuals with higher intake of seafood exhibit lower longitudinal rate of change in HV compared to those with no consume (THV, β: -0.004514; P<0.05; RHV, β: -0.005527, P<0.05). In subgroup and sensitivity analyses, there was not a significantly alteration. A moderate increase in the intake and proportion of animal protein in the diet, especially from seafood, is associated with a lower longitudinal change rate in hippocampus volume.

Bicycle transport stands at the forefront of sustainable mobility initiatives, a cornerstone of European Union policy. While support for cycling is crucial, its advancement hinges upon the presence of high-quality, secure infrastructure for cyclists. Safety emerges as a paramount concern, influencing the viability of cycling as a mode of transportation, yet its perception varies among user demographics. Considering this, this article endeavours to examine and contrast cycling infrastructure across various European nations, alongside analysing statistics pertaining to fatal traffic accidents involving cyclists. In our paper, we have chosen the Netherlands as a representative of building quality cycling infrastructure with many years of experience, and two countries that are developing conditions for cyclists, Slovakia and Poland. We have applied a correlation between the length of the transport infrastructure and the number of cyclists killed. The findings point to the substantial correlation between the quality of cycling infrastructure and the incidence of accidents. This correlation underscores the pressing need for robust infrastructure development and safety measures to bolster cycling as a safe and viable transportation option. We have employed the Shapiro-Wilk test for normal distribution to verify the input data and process the Pearson correlation to investigate the correlation between cycling infrastructure and road accident fatalities of cyclists.

(1)Background:The purpose of the study was conducted to investigate the recovery of heart rate according to the performance level and rest interval conditions during 65%1RM bench press exercise;(2)Methods:The subjects of this study were eight healthy male college studentsin their 20 (years) attending University C. 1RM was measured before theexperiment, and the exercise sequence of six rest intervals (30sec, 1, 2, 3, 4,5min) was randomly selected and cross-distribution experiment. The experimental measurement was conducted a total of six times according to the rest interval condition (six conditions) once every 3 days to minimize the learning effect. The data was verified with repeated measures Two-way ANOVA and Contrast's repeated method was applied for post-comparison;(3)Results: First, the amount of exercise and the number of repetitions statistically substantially dropped (p<.001) as the number of sets rose, and a greater decline was shown when the rest periods got shorter. Second, the heart rate after exercise was increased significantly (p<.01) as the set progressed in the long rest interval conditions (4, 5min), and the heart rate after rest was increased statistically significantly (p<,05) as the set progressed in the short rest interval conditions (30sec, 1min). Third, the heart rate recovery rate (%) was able to perform more repetitions than in the short rest interval condition due to the relatively high physiological recovery of the neuromuscular and circulatory system under the long rest interval condition;(4)Conclusions: Therefore, according to the rest conditionduring the 65%1RM bench press exercise conducted in this study, the restinterval condition of 2~3min may be effective in improving muscular endurance,and the rest interval condition of 4~5min may be effective in improving muscular hypertrophy. As a result of this study, there was a significant difference in exercise performance (repetition) and physiological recovery depending on the rest interval condition according to the set progress during the same intensity resistance exercise. Through this, it is judged that it is possible to adjust the amount of exercise appropriate for the purpose of training by adjusting not only the intensity during resistance exercise but also the rest interval between sets.

Accurate estimation of livestock carrying capacity (LCC) and implementing an appropriate actual stocking rate (ASR) is key to sustainable management of grazing adapted alpine grassland ecosystems. Reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here we demonstrate limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in efforts to support more accurate and reliable estimation of LCC and ASR. Results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. Findings from a smaller-scale study that incorporate phenology timings into estimation of annual aboveground biomass reveal that Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000-2005. The methods proposed can be extended to map grassland grazing pressure by predicting LCC and tracking ASR, thereby improving sustainable resource use in alpine grasslands.

The fixed and random effects models are popular methods for identifying the causal effects of treatment. First, I introduce the two-way fixed effects model and show a class of the estimators for the random effects models. Misidentification may occur in these models due to the treatment effect heterogeneity. Then, I propose the differential error component models to address such problems while the models exclude the fixed effects of units and time periods and consider the random effects of units and time periods. Meanwhile, I present a simulation of a staggered design and revisit the application in (De Chaisemartin and D'Haultfoeuille, 2020). The results demonstrate that one additional newspaper increases the 0.31% average effect of the presidential turnout and the 1.05% average effect when the newspapers’ changes are non-negative, where the proposed estimators are robust and efficient.

The redshifted 21 cm line signal is a powerful probe of the cosmic dawn and the epoch of reionisation. The global spectrum can potentially be detected with a single antenna and spectrometer. However, this measurement requires an extremely accurate calibration of the instrument to facilitate the separation of the 21 cm signal from the much brighter foregrounds and possible variations in the instrument response. Understanding how the measurement errors propagate in a realistic instrument system and affect system calibration is the focus of this work. We simulate a 21 cm global spectrum observation based on the noise wave calibration scheme. We focus on how measurement errors in reflection coefficients affect the noise temperature, and how typical errors impact on the recovery of the 21 cm signal, especially in the frequency domain. Results show that for our example set up, a typical VNA measurement error in the magnitude of the reflection coefficients of the antenna, receiver, open cable which are 0.001, 0.001, 0.002 (linear) respectively would result in a 200 mK deviation on the detected signal, and a typical measurement error of 0.48∘, 0.78∘, 0.15∘ in the respective phases would cause a 40 mK deviation. The VNA measurement error can greatly affect the result of a 21 cm global spectrum experiment using this calibration technique, and such a feature could be mistaken for or be combined with the 21 cm signal.

The literature offers limited information on the effect of obesity on the rate of force development, a critical parameter for mobility in older adults. The objectives of this study were; to explore the influence of obesity on RFD in older adults and to examine the association between this neuromuscular parameter and walking speed. 42 older adults were classified into two groups: the control group (CG, n=22; age=81.13±4.02 years; Body mass index (BMI)=25.13±3.35 kg/m²), and the obese group (OG, n=20; age=77.71±2.95 years; BMI=34.46±3.25 kg/m²). Walking speed (m/s) was measured using the 10-meter walking test. Neuromuscular parameters of the plantar flexors were evaluated during a maximal voluntary contraction test using a dynamometer. The RFD was calculated from the linear slop of the force – time curve in two phases: from the onset of the contraction to 50 ms (RFD50-100) and from 100 to 200 ms (RFD100-200). The gait speed was lower in the OG compared to the CG (p

Formula feeding, as an alternative to breastfeeding, offers benefits such as convenience and consistent nutrition. Among the drawbacks of formula feeding, however, is potential dilution or concentration errors in the of formula during preparation that may lead to infant health issues. The present study aimed to investigate the accuracy of caregiver measurements as they prepared infant formula under multiple conditions, compared with manufacturer specifications. A diverse sample of caregivers (N = 84) participated in this cross-over experimental study. Participants hand-scooped infant formula powder and poured water to prepare 4oz and 7oz feedings, using both a standardized set of infant formula products and participants’ own products. Linear mixed effects models were used to estimate fixed effects of target amount (4oz versus 7oz) and products (participant versus researcher) on mean absolute percent error (MAPE) of measurement. Across all conditions MAPE was significantly greater for measuring powder than for water (9.0% vs. 4.4%; p<0.001) with a combined powder and water MAPE at 13.0%. Greater measurement error was associated with the odd-sized 7oz preparation and participants’ own products. We observed considerable variability and substantial error during infant formula preparation, particularly for hand-scooping of powder, which tended toward higher values than the theoretical gold standard.

Academic publishing is undergoing a highly transformative process, and many rules and value systems that were in place for years are being challenged in unprecedented forms leading to the evolution of novel ways of dealing with new pressures. One of the most important aspects of an integrated and valid academic literature is the ability to screen publications for errors during peer review to weed out mistakes, fraud and inconsistencies, such that the final published product represents a product that has value, intellectually, and otherwise. It is difficult to claim the existence of perfect manuscripts. The level of errors that exist in a manuscript will depend on the rigor of the research group and of the peer review process that was used to screen that paper. When errors slip into a final published paper, either through honest error or misconduct, and are not detected during peer review and editorial screening, but are spotted during post-publication peer review, an opportunity is created to set the record straight, and to correct it. To date, the most common forms of correcting the literature have been errata, corrigenda, expressions of concern, and retractions. Despite this range of corrective measures, which represent artificially created corrals around pockets of imperfect literature, certain cases do not quite fit this mold, and new suggested measures for correcting the literature have been proposed, including manuscript versioning, amendments, partial retractions and retract and replace. In this commentary, a discussion of the evolving correction of the literature is provided, as are perspectives of the risks and benefits of such new measures to improve it.

The paper proposes a mechanism for the impact of changes in the key rate on the volume of newly issued loans. The volume depends on the price (interest rates on loans), and the price depends on the key rate and the actual consumption of loans in the previous period (generalized cobweb cycle). The model was estimated by a Kalman filter, adequacy was confirmed by simulation. It is possible to forecast the average rate on loans for a month in advance according to the information published by the Central Bank of the Russian Federation (CB). By playing various scenarios for changing the key rate, it was found that in quiet periods of economic development, the usual laws of supply and demand operate in the loan market and by raising the key rate, you can reduce inflation. In the turbulent (overheated) state of the economy, an increase in the key rate can, on the contrary, provoke an increase in the issuance of loans and unconventional manipulations with the key rate are required.

Average of 41,900 patients are diagnosed annually with staphylococcus bacterial infection in California, 24,089 patients have Methicillin-resistant Staphylococcus Aureus (MRSA) and 17,810 patients have Methicillin-Sensitive Staphylococcus (MSSA). This paper demonstrates that there is a difference in mortality rate due to staphylococcus infection between males and females (P-value<0.05, CI 95%). Male patient diagnosed with S. aureus has 1.3 chance of mortality incidence than female patient. In addition, MRSA infection rate is 1.4 times MSSA infection (P-value<0.05, CI 95%), but the gap of infection is decreasing; however, mortality of both infections combined are more than threefold greater compared to three decades ago.

In real financial markets, the certainty equivalent rate is an important factor for investors to consider. From the value of a specific company’s certainty equivalent rate, investors can judge if this company is worth investing in. In this paper, we will explore how to obtain the value of the certainty equivalent rate so that we can evaluate a given company. First, we will introduce the theoretical support of this paper, which is put-call parity. From put-call parity, we will derive the mathematical expression for the certainty equivalent rate. Second, we will use the mathematical expression for the certainty equivalent rate to perform an empirical study. We will consider some extreme examples, including some of the best- and worst-performing stocks up to this point in 2023. Based on these stocks’ data, we will visualize the performance of these stocks and explain the meaning of the data. Finally, we will apply the results to a very popular industry—the electric vehicle industry—to explore the current electric vehicle market situation in the U.S.A.

During the hand-eye calibration process of the manipulator, the Euclidean distance error of the calibration results cannot be calculated because the true values of the hand-eye conversion matrix cannot be obtained. In this paper, a new method of projection error analysis is presented. Error analysis is carried out by using a priori knowledge that the location of AR markers is fixed in the calibration process. The coordinates of the AR marker center point are reprojected to the pixel coordinate system, and then compared with the true pixel coordinates of the AR marker center point obtained by corner detection or manual labeling in order to obtain the Euclidean distance between the two coordinates as the basis for error analysis. Experimental results show that the proposed optimization method can greatly improve the accuracy of hand-eye calibration results.

: To improve the efficiency of rhizobial inoculation operations and address the current situation of rhizobial inoculation being labor-intensive, a spraying and control system for variable rate application (VRA) of liquid rhizobia was developed. The system can accurately regulate the spraying rate of liquid rhizobia according to the desired rate of rhizobia by incremental Proportional Integral Derivative (PID) closed-loop control algorithm, making it particularly suitable for precise spraying with small amounts. In the laboratory experiment of precise flow control, variation coefficient of spraying nozzle from 0.10 MP to 0.20 MP pressure were no more than 1.2%. A clear linear relationship was observed between the spraying rate and pressure. The maximum response time was 1.93 seconds, while the average response time was 1.62 seconds when controlling the spraying system for small amounts. Field trial was conducted in Heilongjiang Agricultural Xianghe Farm of China. Average seeds number and 100-seed weight of soybean plant with rhizobia inoculation treatment were better than control group, and average yield of spraying liquid rhizobia agent with full base fertilizer increased by 232.5 kg/hm2, representing a significant 7.4% improvement over the control group. The results of these trails clearly showed the positive effect of the spraying and control system for liquid rhizobial inoculation, which has superior to existing inoculation manners, and provides an important technical support for the widespread adoption of rhizobia technology.

: Greenhouse plants are usually grown in containers filled with growing media. Peat is the main component of the growing media used in greenhouses. However, peat substrate is made from peat from drained peatlands, which is a limited resource. Wood fiber can be an alternative to use as a growing media for controlled-climate crops. The chemical properties of the growing media interact and change continuously due to the small volume of the growing media, which is limited by the vegetative container. Therefore, this study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with peat substrate on the content and uptake of nutrients required by plants. Cucumbers (Cucumis sativus L.) were cultivated in growing media of peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) conventional fertilization (CF); (2) N13; (3) N23; (4) N30. Fertilization of cucumbers with different rates of nitrogen influenced the nutrient uptake. Plants grown in WF/PS 50/50 and WF/PS 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by plants grown not only in the wood fiber but also in combinations with peat substrate. Growing plants in wood fiber and fertilizing them with N13 can result in optimum uptake of micronutrients. The number and mass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and mass of fruits per plant was obtained significantly higher when cucumbers were grown on WF/PS 50/50 growing media with additional N13 fertilization.

In this study the impact of terrorist attacks on exchange rate is estimated. Particularly, the study focuses on Turkish terrorist attacks and its implication on Turkish lira versus pound sterling exchange rate. In order, to find the causal effect the study employed Autoregressive distributive lag (ARDL) bound testing approach as an estimation technique. Accordingly, the analysis reveals that terrorist attack has a negative impact on the exchange rate in both short and long-run. However, the negative effect of terrorism tends to be small in both the short-run and long-run. More precisely, terrorist attack depreciates the exchange rate between Turkish lira and pound sterling by approximately 0.00072 in the next trading day. The long-term effect also shows that terrorist attack depreciates the exchange rate on average by 0.00212.

In this paper we compare the effects of forecasting demand using individual (disaggregated) components versus first aggregating the components either fully or into several clusters. Demand streams are assumed to follow autoregressive moving average (ARMA) processes. Using individual demand streams will always lead to a superior forecast compared to any aggregates, however we show that if several aggregated clusters are formed in a structured manner then these subaggregated clusters will lead to a forecast with minimal increase in mean-squared forecast error. We show this result based on theoretical MSFE obtained directly from the models generating the clusters as well as estimated MSFE obtained directly from simulated demand observations. We suggest a pivot-algorithm, that we call Pivot Clustering, to create these clusters. We also provide theoretical results to investigate sub-aggregation, including for special cases such as, aggregating MA(1) streams and ARMA streams with similar or same parameters.

This study proposes self-calibration of capacitor mismatch errors for high-resolution pipeline analog-to-digital converters (ADCs). The proposed calibration circuit recursively amplifies the capacitor mismatch error by re-utilizing a multiplying digital-to-analog converter in a pipeline stage without increasing the circuit complexity, and the amplified error voltage is converted into digital code by utilizing the remaining pipeline stages. Error correction is performed by subtracting the digital code from the ADC output during normal operation. A prototype of a 12-bit pipeline ADC is fabricated in a 0.18 µm standard CMOS process. The ADC comprises eight 1.5-bit stages, followed by a 4-bit flash ADC as the final stage; the capacitor mismatch errors in the first two pipeline stages are corrected by utilizing the proposed self-calibration technique. At a sampling rate of 30 MS/s, the measured differential and integral nonlinearities improve from +0.82/-0.75 and +1.12/-1.79 to +0.45/-0.41 and +0.47/-0.91 after calibration, respectively. Further, the measured dynamic performances improve significantly with calibration. For an input frequency of 2.09 MHz, the ADC achieves a spurious-free dynamic range (SFDR) and signal-to-noise and distortion ratios (SNDR) of 69.3dB and 63.9dB before calibration, respectively. After calibration, the SFDR and SNDR increase to 84.1dB and 68.9dB, respectively, and the effective number of bits is approximately 11.1bit. The power consumption of the ADC is 35mW under 1.8V supply, and the resulting figure of merit is 514fJ/conversion step.

The Kwee van Woerden (KvW) method for the determination of eclipse minimum times has been a staple in eclipsing binary research for decades, due its simplicity and independence of external input parameters. However, its estimates of the timing error have been known to be of low reliability. During the analysis of very precise photometry of CM Draconis eclipses from TESS space mission data, KvW’s original equation for the timing error estimate produced numerical errors, which evidenced a fundamental problem in this equation. This contribution introduces an improved way to calculate the timing error with the KvW method. A code that implements this improved method, together with several further updates over the original method is presented as well. An example application on the CM Draconis light curves from TESS is given, where we show that its timing error estimates of about 1 second are in excellent agreement with error estimates obtained by other means.

(1) Background: Heart rate variability (HRV) has emerged as a potential prognostic tool in cardiovascular diseases, offering individualized insights into autonomic nervous system activity. However, real-time HRV data during the acute phase of STEMI, particularly during percutaneous coronary intervention (PCI), was not previously explored. (2) Methods: This single-center, observational cohort study included 104 STEMI patients undergoing primary percutaneous coronary intervention (PCI). HRV parameters were measured using a wearable device (CE marked) during PCI, capturing dynamic responses. The association between HRV parameters and adverse outcomes, including in-hospital mortality and major adverse cardiovascular events (MACE), were assessed. (3) Results: HRV parameters significantly decreased after myocardial revascularization, particularly SDNN, RMSSD, pNN50, HF, SD1, and SD2/SD1 ratio. Significant associations were found between reduced SD2/SD1 ratio, approximate entropy, and adverse outcomes, including increased in-hospital mortality and MACE. The SD2/SD1 ratio was significantly lower in patients who died during the hospital stay (p = 0.008) compared to survivors. Approximate entropy was also significantly lower in deceased patients (p = 0.019). (4) Conclusions: Real-time HRV monitoring using wearable technology offers valuable data regarding dynamic physiological changes during primary PCI. The integration of HRV into routine clinical practice, facilitated by wearable devices, could improve the risk stratification and event prediction in STEMI patients.

We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes.

The fractional reduced differential transform method is a finite iterative method based on infinite fractional expansions. The result obtained is the approximation of the real value. There are few reports on the approximate error and the applicable condition. In this paper, according to the fractional expansions, we study the factors related to the approximate errors. Our research shows that the approximate errors relate not only to fractional order but also to time $t$ and increase rapidly with time $t$. This method can only be applied within a certain time range and the time range is relevant to fractional order and fractional expansions. Then, many obtained achievements may be incorrect if the applicable conditions are not satisfied. Some examples presented in this paper verify our analysis.

Cooperative non-orthogonal multiple access (CNOMA) has been recently adapted with energy harvesting (EH) to increase energy efficiency and extend the lifetime of energy-constrained wireless networks. In EH, two protocols are usually used for CNOMA, namely, power splitting (PS) and time switching (TS). In order to improve the existing EH protocols, this paper investigates a hybrid EH protocol-assisted CNOMA, which is a combination of the two aforementioned main existing EH protocols. The end-to-end bit error rate (BER) and outage probability (OP) expressions of users in our scheme are derived over Nakagami-m fading channels. For the sake of comparison and to highlight the achievable gains, the performance of the hybrid EH (HEH) protocol is compared with the benchmark schemes (i.e., existing EH protocols and no-EH). Based on extensive simulations, it is shown that the analytical results match perfectly with simulations which demonstrates the correctness of the derivations. Numerical results clearly indicate the superiority of the proposed scheme over all the considered benchmarks. In addition, we discuss the optimum value of EH factors to minimize the error and outage in the proposed scheme and show that an optimum value can be obtained according to channel parameters.

In this work, we derive the spectral efficiency, secrecy outage probability, and bit error rate of a communication system assisted by a large intelligent surface (LIS). We consider a single-antenna user and an array of antennas at the transmitter side and the possibility of a direct link between transmitter and receiver. Additionally, there is a single-antenna eavesdropper with a direct link to the transmitter, which is modeled as a Nakagami-m distributed fading coefficient. The channels from transmitter to the LIS and from the LIS to the user may or may not have the line-of-sight (LoS) and are modeled by the Nakagami- m distribution. Moreover, we assume that the LIS elements perform non-ideal phase cancellation leading to a residual phase error that assumes a Von Mises distribution. We show that the resulting channel can be accurately approximated by a Gamma distribution whose parameters are analytically estimated using the moments of the equivalent signal-to-noise ratio. We also provide an upper bound for the error probability for M-QAM modulations. With the derived formulas, we analyze the effect of the strength of the LoS link by varying the Nakagami parameter, m.

Different values of the Hubble constant for extragalactic objects are not considered here. We give a number of examples of the extreme accordance of expansion rates of different fields of knowledge with the cosmological expansion rate. The coincidence of the expansion rates means that a common cause is almost inevitable. All these examples are gravitationally bound in themselves and in this case are subject to cosmological expansion. According to standard theory, this should not happen. We therefore question the common boundary of gravity and expansion for both theoretical and observational reasons and conclude that all gravitationally dominated objects participate in cosmological expansion or scale drift, contrary to general doctrine. The space expands with its contents while numerically maintaining distance, radius, rotation time and density. What is generally interpreted as an expansion is obviously a scale drift with a drift rate that corresponds to the size of the Hubble constant. The Earth is subject to expansion and scale drift. This results in numerically constant measured values. This drift apparently also applies to distant galaxies and other objects. The cosmological red shift is not interpreted here as a Doppler effect and numerical increase in distances, but in accordance with standard theory as an expansion or drift of the space-time scale. The expansion of the radii of galaxies makes the assumption of dark matter superfluous. The continents and our everyday environment are not subject to expansion or scale drift.

Demographic and socio-economic developments couple with other requirements to satisfy human needs result in rapid urban expansion sometimes with increasing rate of surface extent greater than the rate of growth of population, that result in continuous sealing of ground surface thereby affecting ecosystem services. This study applied remote sensing toward achieving the progress of SDGs and stage to determine the ratio of the rate of land consumption to the rate of population growth of Gombe metropolis. QGIS 2.18 was used for the image processing and classification analysis for the key Landsat ETM+ (Enhanced Thematic Mapper), Impervious Surface Indices and population data to inform on the urban trend and LCR/PGR for the periods 2000-2005, 2005-2010, and 2010-2015. The result appears that the LCR/PGR for the periods of study show split trends. During 2000-2005 the result shows that the study area expanded outward with LCR/PGR of 1.2. The result also indicate that during 2005-2010, the study area densified with little expansion with the LCR/PGR of 0.8. The result further reveals that during 2010-2015 the LCR/PGR reached 1.8. Pointing that the study area expanded outward with the rate of ground sealing getting high.

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38% on the full dataset and 47% on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4% after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data.

In this paper, we propose a pre-configured error pattern ordered statistics decoding (PEPOSD) algorithm and discuss its application to short cyclic redundancy check (CRC)-polar codes. Unlike the traditional OSD that changes the most reliable independent symbols, we regard the decoding process as testing the error patterns, like guessing random additive noise decoding (GRAND). Also, the pre-configurator referred from ordered reliability bits (ORB) GRAND can better control the range and testing order of EPs. Offline-online structure can accelerate the decoding process. Additionally, we also introduce two orders to optimize the search order for testing EPs. Compared with CRC-aided OSD and list decoding, PEPOSD can achieve a better trade-off between accuracy and complexity.

The intercomparison among different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde, radio occultation (e.g. GNSS-RO) and atmospheric model outputs (e.g. ERA5 reanalysis). Since instruments and/or measures are not perfectly collocated, the miss-collocation uncertainty must be considered in the related intercomparison uncertainty budgets.

Culture exploits the acquisition of meaningful content by crafting regimes of shared attention, determining what is relevant, valuable, and salient. Culture changes the field of relevant social affordances worthy of being acted upon in a context-sensitive manner. When relevant affordances are highly weighted, their attentional capture and their salience increase the probability of them being enacted due to the associated expectation for minimizing prediction error. This process is known as active inference. In the digital era, individuals need to infer the action-related attributes of digital cues, here characterized as digital affordances. The digital affordances of digital social platforms are of particular interest here. Digital social affordances are defined as online possibilities of social interactions. By their own nature, these are salient because they are related to social interactions and relevant social cues. However, the problem of digital social platforms is that they are not equivalent to situated social interactions because their structure is built, mediated, and defined by third-parties with diverse interests. The third-parties behind the digital social platforms are using the same mechanism exploited by culture to manipulate the shared patterns of attention. Moreover, digital social platforms are deliberately designed to be hyper-stimulating, making digital social affordances highly rewarding and increasingly salient. This appropriation, for economic purposes, is an issue of great importance, especially as the COVID-19 pandemic brought deep global changes, pushing societies to an online digital way of life. Here, we examined different types of digital social affordances under an active inference view, placing them into two categories, those for self-identity formation, and those for belief-updating. This paper aims to analyze digital social affordances in light of the prediction error dynamics they might elicit to their users. Although each of the analyzed digital social affordances allows different epistemic and instrumental digital actions, they all share the characteristic of having an "easy" and a fast expected rate of error reduction. Here, we aim to provide a new hypothesis about how the design behind digital social affordances is built on our natural attractiveness to minimize prediction error and the resulting positive embodied feelings when doing so. Finally, it is suggested that because digital social affordances are becoming highly weighted in the field of affordances, this might be putting at risk our context-sensitive grip on a rich, dynamic and varied field of relevant affordances.

Fundamentals of Laparoscopic Surgery (FLS) is a training module designed to provide basic surgical skills. During skill training of the FLS "suturing and intracorporeal knot-tying" task – the most difficult among the five psychomotor FLS tasks, learning from errors is one of the basic principles of motor skill acquisition where appropriate contextual switching of the brain state on error is postulated. This study investigated changes in the brain state following an error event based on the fusion of simultaneously acquired functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. Here, human error processing is postulated to differentiate experts from novices based on the differences in the error-related chain of mental processes. Thirteen right-handed novice medical students and nine expert surgeons participated in this study. Error-related microstate analysis was performed using 32-channel EEG data at a high temporal resolution. Six microstate prototypes were identified from combined EEG data from experts and novices during the FLS task. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10 sec error epoch was significantly affected by the skill level (p<0.01), microstate type (p<0.01), and the interaction between the skill level and the microstate type (p<0.01). Then, the EEG band power (1-40Hz) related to slower oxyhemoglobin (HbO) changes were found using regularized temporally embedded Canonical Correlation Analysis of the fNIRS-EEG signals. The HbO signal from the fNIRS channel overlying ‘Frontal_Inf_Oper_L’, ‘Frontal_Mid_Orb_L’, ‘Postcentral_L’, ‘Temporal_Sup_L’, ‘Frontal_Mid_Orb_R’ cortical areas from Automatic Anatomical Labelling showed significant (p<0.05) difference between experts and novices in the 10-sec error epoch. Here, the frontal/prefrontal cortical areas are postulated to be related to the perception and the activation of the primary somatosensory cortex at the postcentral cortical area is hypothesized to be related to the action underpinning perception-action coupling model for the error-related chain of mental processes. Therefore, our study highlighted the importance of error-related brain states from portable brain imaging when comparing complex surgical skill levels.

Capacity Expansion Models (CEMs) are optimization models used for long-term energy planning on national to continental scale. They are typically computationally demanding, thus in need of simplification, where one such simplification is to reduce the temporal representation. This paper investigates how using representative periods to reduce the temporal representation in CEMs distorts results compared to a benchmark model of a full chronological year. The test model is a generic CEM applied to Europe, equipped with a novel formulation for storage in model versions with reduced temporal representation. We test the performance of reduced models at penetration levels of wind and solar of 90%. Three measures for accuracy are used: (i) system cost, (ii) total capacity mix and (iii) regional capacity. We find that: (i) the system cost is well represented (~5% deviation from benchmark) with as few as ten representative days, (ii) the capacity mix is in general fairly well (~20% deviation) represented with 50 or more representative days, and (iii) the regional capacity mix displays large deviations (>50%) from benchmark for as many as 250 representative days. We conclude that modelers should be aware of the error margins when presenting results on these three aspects.

Obesity is a risk factor for the development of gestational diabetes mellitus (GDM). However, the most optimal type of nutritional intervention to prevent GDM in high-risk women is not clearly defined. This study investigates if nutritional treatment based on the Mediterranean diet (MedDiet) before 12th gestational week (GW) in women at high risk due to a body mass index (BMI)≥25kg/m2, reduces the rate of GDM and metabolic syndrome (MetS) at 3 years postpartum. We performed a post-hoc analysis of the San Carlos Gestational Prevention Study. A total of 735 women with BMI≥25kg/m2 were evaluated between 2015-2018, 246 in standard diet control group (CG) and 489 in MedDiet intervention group (IG). The rate of GDM was significantly lower in IG compared to CG (25.1%vs31.7%;p=0.037). Postnatal follow-up was completed by 141 women in CG (57%) and 312 women in IG (64%). At 3 years postpartum, we observed a reduction (relative risk 95% confidence interval) in the rates of impaired fasting glucose (IFG) (0.51 (0.28-0.92);p=0.019), obesity (0.51 (0.28-0.92),p=0.041), waist circumference (WC)≥89.5cm (0.54 (0.31-0.94);p=0.022) and MetS (0.56 (0.33-0.94);p=0.003). MedDiet reduces the rate of GDM and postpartum MetS in women with BMI)≥25kg/m2, suggesting that its implementation should be routinely recommended from the first GWs.

Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and compares existing algorithms for line fitting applicable also in case of strong covariances between the coordinates at each single data point, which must not be neglected if range-bearing sensors are used. Besides, particularly the determination of the covariance matrix is considered, which is required for stochastic modeling. The main contribution is a new error model of straight lines in closed form for calculating fast and reliably the covariance matrix dependent on just a few comprehensible and easily obtainable parameters. The model can be applied widely in any case when a line is fitted from a number of distinct points also without a-priori knowledge of the specific measurement noise. By means of extensive simulations the performance and robustness of the new model in comparison to existing approaches is shown.

We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular we use the variance of passive observation–minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin.

Inflation and exchange rates have great influence on consumer prices especially on imports and exports. Exchange rate fluctuations create inefficiency and distort world prices whereas changes in inflation rates have a direct impact on consumer goods prices which incidentally include exchange rates. There is a direct interdependence between inflation and exchange rates and this paper is aimed at investigating this relationship in dynamic context. It tries to find out how changes in inflation and exchange rates impact on another by adopting the econometric and copula approaches. Both inflation and exchange rates data are susceptible to volatility clustering, possess fat tails and are skewed coupled with conditional heteroskedasticity. Hence we model the univariate distributions by using ARMA$(p,q)$-GARCH$(x,y)$ so as to capture the most important stylized features of inflation and exchange rates. A bivariate model is then constructed by coupling the marginal distributions of inflation and exchange rates using the survival Clayton copula. Empirical results from monthly inflation and exchange rates data show positive correlation between the two based on Kendall $\tau$ test which confirms that a change in inflation results in change of exchange rates an vice versa hence there is co-movement. Furthermore, by the Granger causality test, exchange rates spikes cause changes in inflation rates. The results of the study have implications on economic policy design and hedging strategies for traders on imports and exports.

Background. The multisystemic features of Down syndrome (DS) in children are accompanied by an immunodeficiency, making them susceptible to infections and immune dysregulation with autoimmune, allergic, inflammatory, and hematological complications. This study was aimed at a better understanding of the abnormalities within the B and T cell compartments and their correlations with clinical immunophenotypes. Methods. Medical records of 35 DS children were retrospectively reviewed, referring to clinical symptomatology including history of infections, immune dysregulation disorders, and humoral and cellular immune response. Results. While the etiology of respiratory tract infections included typical viral and bacterial pathogens, SARS-CoV2-induced inflammatory disease and syndromic immunodeficiency contributed significantly to deterioration of the clinical course. Allergic diseases in the form of asthma, allergic rhinitis, and alimentary allergy were the most frequent manifestations of immune dysregulation and were followed by autoimmune disorders, such as Crohn disease, celiac disease, autoimmune thyroiditis, and alopecia, as well as inflammatory disorders, balanitis xerotica obliterans and lymphadenopathy, and a hematological disorder of myelopoiesis. Deficiency of serum immunoglobulin levels, reduced numbers of naïve B cells, and non-switched memory B cells along with low naïve T helper cells and significantly reduced regulatory T helper cells were the most prominent immune abnormalities. Conclusions. The loss of naïvete in B and T lymphocyte compartments with a deficiency of regulatory T cells may be underpinning pathomechanisms for the skewed immune response. The clinical immunophenotype in DS is complex and represents syndromic inborn error of immunity with immune dysregulation.

In this paper, the Phase-Scheduled-Command FXLMS algorithm with the phase error between the disturbance and command signal is analyzed in detail. The influence of the phase error on the convergence time constant, convergence rate, and performance of convergence is explained for both stationary and nonstationary disturbance signals case. For stationary disturbance, the phase error slightly increases the convergence rate but heavily increases the distance of the optimum vector from the initial value, leading to poor convergence time constant performance. For nonstationary disturbance, the existence of phase error leads to poor convergence performance in every step, resulting in poor sound profiling performance. And the estimation of the phase error influence is developed in the closed form. Simulations are performed to demonstrate the validity of the analysis results.

Spatial regression models have garnered significant attention across several disciplines, including functional magnetic resonance imaging analysis, econometrics, home price analysis, and many other domains. The phenomenon of sparsity is often found in nature, when a limited number of factors contribute significantly to the overall variation. Spatial regression models frequently use sparsity to indicate less complex computational and more superficial covariance structures. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach for estimating the vector of regression coefficients in the spatial error model, taking into consideration of the prior knowledge that some coefficients are insignificant and there is multicollinearity among the regressors. It also introduces pretest and shrinkage ridge estimators for spatial error regression models, evaluating their performance compared to traditional maximum likelihood estimators. It also assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.

Tipping bucket rain gauges (TBRs) have been, and apparently will continue to be one of the most widely used pieces of equipment for rainfall monitoring, being frequently used for the calibration, validation and downscaling of radar and remote sensing data, due to their major advantages–low cost, simplicity, and low energy consumption. Thus, many works have focused and continue to focus on their main disadvantage–measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques in Section 2, summarizing TBR measurement uncertainties in Section 3, focusing on calibration, and error reduction strategies in Section 4, a discussion and perspectives in Section 5, and conclusions in Section 6, providing an overview of the of the state of the art and future perspectives of the technology.

We propose an approach for estimating the error in depth data provided by generic 3D sensors, which are modern devices capable of generating an image (RGB data) and a depth map (distance) or other similar 2.5D structure (e.g. stereo disparity) of the scene. Our approach starts capturing images of a checkerboard pattern devised for the method. Then proceed with the construction of a dense depth map using functions that generally comes with the device SDK (based on disparity or depth). The 2D processing of RGB data is performed next to find the checkerboard corners. Clouds of corner points are finally created (in 3D), over which an RMS error estimation is computed. We come up with a multi-platform system and its verification and evaluation has been done, using the development kit of the board nVIDIA Jetson TK1 with the MS Kinects v1/v2 and the Stereolabs ZED camera. So the main contribution is the error determination procedure that does not need any data set or benchmark, thus relying only on data acquired on-the-fly. With a simple checkerboard, our approach is able to determine the error for any device. Envisioned application is on 3D reconstruction for robotic vision, with a series of 3D vision sensors embarked in robots (UAV of type quadcopter and terrestrial robots) for high-precision map construction, which can be used for sensing and monitoring.

Language is an essential aspect of teaching and learning mathematics. It is necessary for communicating, transmission of concepts and ideas, and formation of meaning of mathematical concepts. In mathematics, besides symbols, which are usually common in different languages, words and expressions are used, which may invoke different concept images to students in various languages. Some words are used in mathematics and in everyday language with different meanings, while others are used only in mathematics or in mathematics and other disciplines in similar but non-identical ways. In Mathematical Analysis, the used vocabulary is gradually enhanced, and the concepts are defined in a more formal way. In the current study, the language used regarding mathematics of change is examined, focusing on rate of change and in relation to misconceptions of students.

We explored the dynamics of the two interacting information systems. We show that for the Markovian marginal systems the driving force for information dynamics is determined by both the information landscape and information flux. While the information landscape can be used to construct the driving force to describe the equilibrium time reversible information system dynamics, the information flux can be used to describe the nonequilibrium time-irreversible behaviours of the information system dynamics. The information flux explicitly breaks the detailed balance and is a direct measure of the degree of the nonequilibriumness or time irreversibility. We further demonstrate that the mutual information rate between the two subsystems can be decomposed into the equilibrium time-reversible and nonequilibrium time-irreversible parts respectively. This decomposition of the mutual information rate (MIR) corresponds to the information landscape-flux decomposition explicitly when the two subsystems behave as Markov chains. Finally, we uncover the intimate relationship between the nonequilibrium thermodynamics in terms of the entropy production rates and the time-irreversible part of the mutual information rate. We found that this relationship and MIR decomposition still hold for the more general stationary and ergodic cases. We demonstrate the above features with two examples of the bivariate Markov chains.

Background: the study aimed at comparing the prognostic importance of Heart Rate (HR) and of QT corrected (QTc) according to Fridericia, Framingham and Bazett methods, in a large non-selected population. Methods: the analysis of digital Electrocardiograms archived from 2008 to 2022 in the metropolitan area of Modena, Italy, was made. The population under study was divided into three age-groups and survival analysis was performed. Results: 131.627 patients were enrolled and during the follow-up (mean 1641.4 days), all-cause mortality was 8.9%. Both HR and QTc were associated with mortality. All-cause mortality significantly increased from HR values greater than 81 BPM and from QTc values greater than 440 msec in young subjects and 455 msec in old subjects (values of the 75th percentiles/optimal operating point). Cox analysis confirmed the better prognostic value of Bazett’s QTc and of HR in the whole population and in the three age-groups. Conclusion: Bazett’s method performed better than other methods but unexpectedly HR had the same or a better correlation with all-cause mortality. Since HR is simple and readily available, its evaluation should be improved. However, reference values for QTc and for HR are difficult to define causing many confounding factors and further population-study are required.