Correct Pattern Research Articles

Exploring the Ecological Distribution and Antibiotic Resistance Profiles of Klebsiella Species in Diverse Environmental Niches

Antimicrobial resistance is a growing problem in bacterial pathogens, particularly in hospital-acquired nosocomial infections. Klebsiella is an important pathogen that causes various infections in hospitalized immunocompromised patients. Therefore, this study aims to fill this knowledge gap and provide insights into the correct detection and antibiotic resistance pattern of these organisms. we will investigate the spread of pneumonia bacteria, Klebsiella, isolate and identify it from different environmental sites, including human, soil, and water, also study the characteristics of antibiotic resistance and find similarities and differences between bacterial isolates. Finally, we detect the associated antibiotics resistance gene (CTX-M), using the genetic RT-PCR method. The study provides valuable information on the prevalence of antibiotic resistant Klebsiella spp. in different clinical and environmental sites, which can help in the development of effective strategies to control the spread of these bacteria. The study found that among all 86 isolated strains of Klebsiella spp. it was found that K. pneumoniae represents the major abundant strain in both clinical and environmental samples by percentages 41.5% and 51.5% respectively. It concerns also the highest ESBL production 71.7%, along with 58% CTX-M gene expression

Physics-informed neural network for diffusive wave model

The diffusive wave model (DWM), a nonlinear second-order simplified form of the shallow water equation, has been widely used in hydraulic, hydrologic and irrigation engineering. Solution of the forward problem of the DWM can be utilized to predict evolution in water levels and discharge. Solution of its inverse problem allows for the identification of crucial parameters (such as Manning coefficient, rainfall intensity, etc.) based on observations. This paper applied the physics-informed neural network (PINN) with novel improvements to solve the DWM for both forward and inverse problems. In the forward problem, compared to traditional numerical methods, PINN was able to predict the evolution at any location. In the inverse problem, PINN provided a simple and efficient solution process. In order to overcome the gradient explosion in the training process caused by the characteristics of the DWM, the stop-gradient technique was adopted to train the neural network. To improve the estimation of DWM parameters, the concept of time division was developed, and a new network structure was proposed. To verify the effectiveness of PINN and its improved algorithm for DWM, seven examples were simulated. The PINN solutions for forward problems were compared with the results obtained by classical numerical methods, while the correct rainfall pattern was identified for the inverse problem.

Evaluation on the Level of Functional Movement Screen (FMS) and Functional Training VIS-À-VIS Sprint Performance of College Track and Field Athletes

This study selected 56 sprint student-athletes from the track and field team of Hechi University in Guangxi, China, as test subjects. Before the experiment, all athletes were assessed with the Functional Movement Screen (FMS) test, and a detailed analysis and comparison of the athletes' FMS scores were conducted from an objective perspective. After 12 weeks of functional training intervention, a second FMS test assessment was carried out on the participating athletes. The survey results showed that before the training, the movement quality and functional movement ability of the student-athletes were average. Among them, the scores for core stability, lower limb explosive power, and multi-joint movement tests were evaluated as 3 points, showing excellent core stability and lower limb strength. However, the scores for agility, upper limb coordination, and the 100-meter test were rated as 2, which was average. Before and after training, the overall average score of the Functional Movement Screen (FMS) for the student-athlete respondents increased from 15.4500 to 18.1786, and the t-value was -14.140, indicating a significant difference in functional movement levels before and after training. Participants showed noticeable improvements in flexibility, stability, and overall movement quality after the training intervention. Data from the 100-meter test showed that the average score before training was 2.3571 and post-training was 2.9464, with a calculated t-value of -8.883, indicating a significant difference in sprint performance before and after the training. Therefore, introducing this new theory of FMS into future sprint teaching and training practice can not only help correct athletes' physical function deficits and cultivate correct movement patterns, but it can also serve as an effective complement to track and field training methods, providing a theoretical basis for scientifically-informed coaching.

PENGUATAN KAPASITAS TOKOH AGAMA DALAM UPAYA PENANGGULANGAN STUNTING WILAYAH KABUPATEN KUPANG ALOR

The problem of stunting has the potential to disrupt the quality of human resources and is related to health levels and even child mortality. In this case, religious leaders are parties who play a direct role, especially in preventing stunting through shaping family consumption patterns in religious service areas. Family mothers who cultivate and implement nutritious food consumption patterns can prevent family members, including children under five, from nutritional problems, including stunting. The activity was carried out in Alor Regency on 21-23 October 2023 with the aim of strengthening Christian, Catholic, Hindu and Islamic religious leaders in carrying out service duties to families, especially family mothers, in order to increase knowledge about consuming diverse, nutritious, balanced and safe food. and be able to take preventive action at the stage of preparing nutritious food for family consumption. Implementation uses the "Education and/or counseling" method. Where, all religious leaders are given teaching or material with the aim that they can determine the source of the problem and form services related to overcoming and preventing nutritional problems including stunting through choosing healthy, varied and safe types of food. The evaluation process was carried out before and after the activity to measure the abilities and changes in attitudes of 60 religious leaders regarding the counseling material. The pre-test results found that the majority (69.7%) of religious leaders already knew about stunting but did not understand consumption patterns including the type, quantity and correct method of food processing and the impact on the incidence of stunting. However, after confirmation from the results of the counseling, it was discovered that 100% of religious leaders could understand the correct consumption patterns. This starts from indicators of the ability to choose the type of food, compiling a simple but nutritious menu using a processing method that is friendly to the availability of sufficient and appropriate nutritional content as needed and can ensure that the amount of food consumed is according to the needs per day per age per gender of family members.

Baseline Correction of Ground Motions for Linearly Detrended Accelerograms

ABSTRACT By revisiting the accelerograms from 42 strong-motion stations in Hualien County that recorded the 2018 Mw 6.4 Hualien earthquake, we identified that the released “raw accelerograms” were linearly detrended to remove baselines. Baseline correction for these linearly detrended accelerograms is challenging, because the baseline drift patterns are more complex than typically recognized ones. To deal with this problem, here we propose a jerk correction pattern in addition to our previously proposed tilt correction pattern and cumulative correction pattern. The modified baseline correction procedure corrects all 126 accelerograms for three directions of the 42 strong-motion stations. The recovered ground displacements are consistent with geodetic measurements, demonstrating complex surface deformations in the close vicinity of the Milun fault.

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

Client and clinician perspectives about a virtual education and exercise chronic disease management programme for people with hip and knee osteoarthritis.

Osteoarthritis (OA) is one of the most prevalent chronic conditions in Canada. Despite the established benefits of non-pharmacological management (education, exercise) for people with OA, many do not receive treatment, resulting in pain, decreased physical function, and poorer quality of life. Virtual programme options grew significantly during the recent pandemic and may provide longer-term opportunities for increased uptake by reaching individuals otherwise unable to participate. This study explored the experiences and perspectives of clients participating in and clinicians providing the Good Life with osteoArthritis: Denmark (GLA:DTM) Canada remote programme. This qualitative descriptive study recruited 10 clients with hip and/or knee OA and 11 clinicians across Canada using purposive sampling. An online pre-interview survey was completed, and individual interviews were conducted, audio-recorded, transcribed verbatim and analysed independently by two researchers using inductive thematic analysis. Coding and analyses were initially conducted separately by group and then compared and combined. Four overarching themes (and 11 subthemes) were identified: (1) Expected and unexpected benefits of virtual programs; (2) Drawbacks to virtual programs; (3) Programme delivery in a virtual world; (4) Shifting and non-shifting perspectives. Although initially sceptical, after completion of the programme, clients were in favour of virtual delivery with many benefits described. Clinicians' perspectives varied about feedback aimed to correct client movement patterns. Clients and clinicians identified important experiential and procedural elements for virtual chronic disease management programs that include education and exercise. Additional work is needed to understand if the GLA:DTM remote outcomes are equivalent to the in-person programme.

Insertion of a neomycin selection cassette in the Amigo1 locus alters gene expression in the olfactory epithelium leading to region-specific defects in olfactory receptor neuron development.

During development of the nervous system, neurons connect to one another in a precisely organized manner. Sensory systems provide a good example of this organization, whereby the composition of the outside world is represented in the brain by neuronal maps. Establishing correct patterns of neural circuitry is crucial, as inaccurate map formation can lead to severe disruptions in sensory processing. In rodents, olfactory stimuli modulate a wide variety of behaviors essential for survival. The formation of the olfactory glomerular map is dependent on molecular cues that guide olfactory receptor neuron axons to broad regions of the olfactory bulb and on cell adhesion molecules that promote axonal sorting into specific synaptic units in this structure. Here, we demonstrate that the cell adhesion molecule Amigo1 is expressed in a subpopulation of olfactory receptor neurons, and we investigate its role in the precise targeting of olfactory receptor neuron axons to the olfactory bulb using a genetic loss-of-function approach in mice. While ablation of Amigo1 did not lead to alterations in olfactory sensory neuron axonal targeting, our experiments revealed that the presence of a neomycin resistance selection cassette in the Amigo1 locus can lead to off-target effects that are not due to loss of Amigo1 expression, including unexpected altered gene expression in olfactory receptor neurons and reduced glomerular size in the ventral region of the olfactory bulb. Our results demonstrate that insertion of a neomycin selection cassette into the mouse genome can have specific deleterious effects on the development of the olfactory system and highlight the importance of removing antibiotic resistance cassettes from genetic loss-of-function mouse models when studying olfactory system development.

A Simple Statistical Postprocessing Scheme for Enhancing the Skill of Seasonal SST Predictions in the Tropics

Abstract Seasonal prediction systems are subject to systematic errors, including those introduced during the initialization procedure, that may degrade the forecast skill. Here we use a novel statistical postprocessing correction scheme that is based on canonical correlation analysis (CCA) to relate errors in ocean temperature arising during initialization with errors in the predicted sea surface temperature fields at 1–12-month lead time. In addition, the scheme uses CCA of simultaneous SST fields from the prediction and corresponding observations to correct pattern errors. Finally, simple scaling is used to mitigate systematic location and phasing errors as a function of lead time and calendar month. Applying this scheme to an ensemble of seven seasonal prediction models suggests that moderate improvement of prediction skill is achievable in the tropical Atlantic and, to a lesser extent, in the tropical Pacific and Indian Ocean. The scheme possesses several adjustable parameters, including the number of CCA modes retained, and the regions of the left and right CCA patterns. These parameters are selected using a simple tuning procedure based on the average of four skill metrics. The results of the present study indicate that errors in ocean temperature fields due to imperfect initialization and SST variability errors can have a sizable negative impact on SST prediction skill. Further development of prediction systems may be able to remedy these impacts to some extent. Significance Statement The prediction of year-to-year climate variability patterns, such as El Niño, offers potential benefits to society by aiding mitigation and adaptation efforts. Current prediction systems, however, may still have substantial room for improvement due to systematic model errors and due to imperfect initialization of the oceanic state at the start of predictions. Here we develop a statistical correction scheme to improve prediction skill after forecasts have been completed. The scheme shows some moderate success in improving the skill for predicting El Niño and similar climate patterns in seven prediction systems. Our results not only indicate a potential for improving prediction skill after the fact but also point to the importance of improving the way prediction systems are initialized.

Dynamic Modeling of Heat Exchangers Based on Mechanism and Reinforcement Learning Synergy

The current lack of a high-precision, real-time model applicable to the control optimization process of heat exchange systems, especially the difficulty in determining the overall heat transfer coefficient K of heat exchanger operating parameters in real time, is a prominent issue. This paper mainly unfolds the following work: 1. We propose a dynamic model for the control and optimization of the heat exchanger operation. By constructing a system to collect real-time operating data on the flow rates and temperatures on both sides of the heat exchanger, the parameter identification of the overall heat transfer coefficient K is performed. Subsequently, by combining this with mechanistic equations, a novel heat exchanger model is established based on the fusion of mechanistic principles and reinforcement learning. 2. We validate the new model, where the average relative error between the model’s temperature output values and the actual measured values is below 5%, indicating the high identification accuracy of the model. Moreover, under variations in the temperature and flow rate, the overall heat transfer coefficient K demonstrates the correct patterns of change. 3. To further enhance the model’s identification accuracy, a study on the reward functions in reinforcement learning is conducted. A model with the Logarithmic Mean Temperature Difference (LMTD) as the reward function exhibits a high identification accuracy. However, upon comparison, a model using the Arithmetic Mean Temperature Difference (AMTD) for relative error as the reward function shows an even higher identification accuracy. The model is validated under various operating conditions, such as changes in the flow rate on the hot side, demonstrating good scalability and applicability. This research contributes to providing a high-precision dynamic parameter basis for the precise control of heat exchange systems, offering significant guidance for the control optimization of actual heat exchange system operations.

Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data

The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction (Rval2 ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions.

Inter-plane feedback coordinates cell morphogenesis and maintains 3D tissue organization in the Drosophila pupal retina.

How complex organs coordinate cellular morphogenetic events to achieve three-dimensional (3D) form is a central question in development. The question is uniquely tractable in the late Drosophila pupal retina, where cells maintain stereotyped contacts as they elaborate the specialized cytoskeletal structures that pattern the apical, basal and longitudinal planes of the epithelium. In this study, we combined cell type-specific genetic manipulation of the cytoskeletal regulator Abelson (Abl) with 3D imaging to explore how the distinct cellular morphogenetic programs of photoreceptors and interommatidial pigment cells (IOPCs) organize tissue pattern to support retinal integrity. Our experiments show that photoreceptor and IOPC terminal differentiation is unexpectedly interdependent, connected by an intercellular feedback mechanism that coordinates and promotes morphogenetic change across orthogonal tissue planes to ensure correct 3D retinal pattern. We propose that genetic regulation of specialized cellular differentiation programs combined with inter-plane mechanical feedback confers spatial coordination to achieve robust 3D tissue morphogenesis.

Changes in gait parameters and pelvic movement during gait under the influence of light loads carried symmetrically or asymmetrically — preliminary research

Introduction: Walking is one of the basic human activities. Taking care of the correct gait pattern is important for maintaining proper body posture and preventing dysfunctions of the musculoskeletal system. Since walking often involves carrying a load, the study aimed to evaluate changes in gait parameters and pelvic movement during gait under the influence of light loads carried symmetrically or asymmetrically in a bag or backpack. Material and methods: The group of 11 women and 15 men aged between 19 and 25 were examined. The BTS G-Sensor device was used. The basic gait parameters were examined, as well as the symmetry coefficient of pelvic motion and the range of pelvic motion in each of the three planes. The test involving walking 30 meters was repeated four times: while walking without a load, with an additional load worn in a backpack on both shoulders, in a bag on the right shoulder, or in a bag worn diagonally on the left shoulder. Each time the load was equal to 10% of the participantʼs body weight. Results: Walking speed, stride length to body height ratio, gait symmetry coefficient, and left and right foot propulsive speed did not change significantly during walking while carrying a load of 10% of body weight, regardless of the method of carrying a bag or backpack. Conclusions: Although carrying a light bag or backpack does not change the basic gait parameters, even such a small load reduces the symmetry of pelvic movement and the range of pelvic motion in the frontal and transverse planes during walking.

Investigation of the functional and biomechanical effect of instrument-assisted soft tissue mobilization technique in individuals with asymptomatic dynamic knee valgus - Randomized controlled trial

Although there are studies showing that myofascial release will increase muscle force production, the contribution of its application alone to muscle force production has not been examined. Aim of the study is to investigate the effect of instrument-assisted soft tissue mobilization (IASTM) on eccentric strength, frontal plane projection angle (FPPA), dynamic (DPS), and static postural stability (SPS), femoral internal rotation (FIR) angle in females with dynamic knee valgus (DKV).A total of 44 recreationally active females with asymptomatic DKV (age: 21,39 ± 1,79, body mass index: 20,09 ± 2,45) participated and were randomly assigned to either control group (CG) or IASTM group (IASTMG).Participants' eccentric contraction strength, FPPA, DPS, SPS, and FIR on the involved leg were measured pre- and post. IASTM application was applied to IASTMG for 6 weeks, twice a week, for 5 min, using Graston Technique® instruments on gluteus medius. CG received no intervention. In comparison of ECS difference values, change in IASTMG was found to be statistically significantly higher than CG (p = .004; p < .01). There was no statistical difference in comparison of FIR and FPPA values (respectively p = .213, p = .360; p < .05). In SPS and DPS evaluation, a statistically significant improvement was observed in favor of IASTMG in comparison of both intergroup and difference values (p < .05 for all). Strength gain without exercise can increase postural stability, but it isn't sufficient to correct faulty movement patterns. We recommend adding IASTM to injury prevention programs, but there is a need to investigate the effect of IASTM with technique correction feedback.

Nonhomologous tails direct heteroduplex rejection and mismatch correction during single-strand annealing in Saccharomyces cerevisiae.

Single-strand annealing (SSA) is initiated when a double strand break (DSB) occurs between two flanking repeated sequences, resulting in a deletion that leaves a single copy of the repeat. We studied budding yeast strains carrying two 200-bp URA3 sequences separated by 2.6 kb of spacer DNA (phage lambda) in which a site-specific DSB can be created by HO or Cas9 endonucleases. Repeat-mediated deletion requires removal of long 3'-ended single-stranded tails (flaps) by Rad1-Rad10 with the assistance of Msh2-Msh3, Saw1 and Slx4. A natural 3% divergence of unequally spaced heterologies between these repeats (designated F and A) causes a significant reduction in the frequency of SSA repair. This decrease is caused by heteroduplex rejection in which mismatches (MMs) in the annealed intermediate are recognized by the MutS (Msh2 and Msh6) components of the MM repair (MMR) pathway coupled to unwinding of the duplex by the Sgs1-Rmi1-Top3 helicase. MutL homologs, Mlh1-Pms1 (MutL), are not required for rejection but play their expected role in mismatch correction. Remarkably, heteroduplex rejection is very low in strains where the divergent repeats were immediately adjacent (Tailless strains) and the DSB was induced by Cas9. These results suggest that the presence of nonhomologous tails strongly stimulates heteroduplex rejection in SSA. DNA sequencing analysis of SSA products from the FA Tailed strain showed a gradient of correction favoring the sequence opposite each 3' end of the annealed strand. Mismatches located in the center of the repair intermediate were corrected by Msh2-Msh6 mediated mismatch correction, while correction of MMs at the extremity of the SSA intermediate often appears to use a different mechanism, possibly by 3' nonhomologous tail removal that includes part of the homologous sequence. In contrast, in FA Tailless strains there was a uniform repair of the MMs across the repeat. A distinctive pattern of correction was found in the absence of MSH2, in both Tailed and Tailless strains, different from the spectrum seen in a msh3Δ msh6Δ double mutant. Previous work has shown that SSA is Rad51-independent but dependent on the strand annealing activity of Rad52. However Rad52 becomes dispensable in a Tailless construct where the DSB is induced by Cas9 or in transformation of a plasmid where SSA occurs in the absence of nonhomologous tails.

The application of Machine and Deep Learning for technique and skill analysis in swing and team sport-specific movement: A systematic review

Abstract There is an ever-present need to objectively measure and analyze sports motion for the determination of correct patterns of motion for skill execution. Developments in performance analysis technologies such as inertial measuring units (IMUs) have resulted in enormous data generation. However, these advances present challenges in analysis, interpretation, and transformation of data into useful information. Artificial intelligence (AI) systems can process and analyze large amounts of data quickly and efficiently through classification techniques. This study aimed to systematically review the literature on Machine Learning (ML) and Deep Learning (DL) methods applied to IMU data inputs for evaluating techniques or skills in individual swing and team sports. Electronic database searches (IEEE Xplore, PubMed, Scopus, and Google Scholar) were conducted and aligned with the PRISMA statement and guidelines. A total of 26 articles were included in the review. The Support Vector Machine (SVM) was identified as the most utilized model, as per 7 studies. A deep learning approach was reported in 6 studies, in the form of a Convolutional Neural Network (CNN) architecture. The in-depth analysis highlighted varying methodologies across all sports inclusive of device specifications, data preprocessing techniques and model performance evaluation. This review highlights that each step of the ML modeling process is iterative and should be based on the specific characteristics of the movement being analyzed.

Influence of Verbal Instruction on Gait Training in Parkinson Disease: A Randomized Controlled Trial.

Verbal instruction is one of the most commonly used methods that therapists use to correct walking pattern for people with Parkinson disease. This study aimed to compare the long-term training effects of two different verbal instructions that either asked the participants to "take big steps" or "strike the ground with the heel" on walking ability in individuals with Parkinson disease. Forty-five participants with Parkinson disease were randomized into the big-step or heel strike group. The participants underwent 12 sessions of treadmill and overground gait training. Throughout the interventions, the big-step group received an instruction to "take big steps," while the heel strike group received an instruction to "strike the ground with your heel." The primary outcome was gait performance, including velocity, stride length, cadence, and heel strike angle. The participants were assessed before, immediately after, and 1 mo after training. Both groups showed significant improvements in gait performance after training. The heel strike group showed continuous improvements in velocity and stride length during the follow-up period; however, the big-step group showed slightly decreased performance. A verbal instruction emphasizing heel strike can facilitate long-term retention of walking performance in people with Parkinson disease.

A deep-learning approach for turbulence correction in free space optical communication with Laguerre–Gaussian modes

Free space optical communication has become increasingly popular in the past decade due to its terahertz bandwidth, unlicensed spectrum, and enhanced security features. This technology has been attracting significant interest from the network community due to its broad applications in future military and civilian protocols. Recently, the use of orthogonal spatial states of the light field, including those with nonzero orbital angular momentum, has increased communication system capacities and bit-transfer rates per photon. However, atmospheric turbulence disrupts the orthogonal spatial modes over propagation. We present a novel deep learning architecture, cGULnet, to mitigate the effects of atmospheric turbulence on spatial multiplexing of Laguerre–Gaussian structured light modes. The model predicts the phase correction patterns required for an adaptive optical system. It is a U-shaped architecture trained adversarially to a patch discriminator similar to conditional generative adversarial neural-networks. The network is trained for exact phase recovery systems without approximating Zernike polynomials, enabling its use in highly phase-sensitive but high-speed spatial multiplexed communication systems. The proposed architecture can predict the successive three frames in a single shot using the previous ten frames. The method was evaluated for a wide range of beam waist sizes in a simulated Laguerre–Gaussian spatial mode multiplexed 1 km free space optical communication link. We have considered 20 dB optical signal-to-noise ratio, with turbulence conditions parameterized by the refractive index structure parameter of Cn2≤5×10−15m−23. It was found to reduce the bit error rate to the International Telecommunication Union recommended forward error correction limit of 3.8×10−3 under high turbulence conditions.

WAFA METHOD IN LEARNING TAHSIN TO READ AL-QURAN IN BINA INSAN PALU INTEGRATED ISLAMIC ELEMENTARY SCHOOL

This article discusses the use of wafa method in learning tahsin to read al-quran in a elementary school in Palu city, Central Sulawesi. The research used a qualitative method with data collection techniques such as direct observation, in-depth interviews, and document analysis. The research findings show that planning for learning the Qur'an using the Wafa method includes the syllabus and learning implementation plan, learning objectives, learning materials, learning media, and evaluation. Applying the Wafa method to learning tahsin in the Qur'an uses quantum teaching: grow, experience, name, demonstrate, repeat, and celebrate. However, some methods include opening, experience, teaching, assessment, and closing. The results of tahsin learning in reading the Qur'an for students using the Wafa method at the Bina Insan Integrated Islamic Elementary School in Palu are that it is easier for students to recognize the letters, students can read the Qur'an well and correctly, students are also able to read the Qur'an using the hijaz tone with the correct pattern, simplified, and students are also able to write the Qur'an with Ustmani khat.