Small Problems Research Articles

THE EFFECT OF ROSE AROMATHERAPY (ROSA CENTIFOLIA) ON SLEEP DURATION FOR INFANTS AGED 0-28 DAYS AT PONCOWATI PUBLIC HEALTH CENTER LAMPUNG TENGAH IN 2022

ABSTRACT Approximately 75% of growth hormone in children is excreted during sleep. In Indonesia, quite a lot of babies have sleep problems, which is around 44.2% of babies experience sleep disorders such as frequently waking up at night, but more than 72% of parents consider sleep disturbances in infants not a problem or only consider it a small problem (Sekartini , 2012; Chandraini, 2021).Based on presurvey data by looking at data on baby sleep duration problems, which was carried out in 2 Puskesmas Kabupaten Lampung Tengah, namely at UPTD Puskesmas Bandar Jaya and UPTD Puskesmas Poncowati, it was found that 12 infants aged 0-28 days had problems sleeping at UPTD Puskesmas Bandar Jaya, while in In Poncowati Health Center, 20 babies aged 0-28 days had problems fulfilling the baby's sleep time, which was marked by fussy babies and crying, babies couldn't sleep if they weren't carried by their mothers.This study was to determine the effect of giving rose aromatherapy on the sleep duration of infants aged 0-28 days at Poncowati Health Center in Lampung Tengah in 2022.The research method was qualitative with questionnaire research instruments, thenumber of samples were 36 respondents (infants aged 0-28 days) taken by purposive sampling technique, data analysis using t test with a two group pretest posttest approach with control group design.Result of the study with an increase in the average quality of baby sleep after beinggiven rose aromatherapy (Rosa Centifolia) at the Poncowati Health Center, Lampung Tengah in 2022 was 33.33% higher than the average increase in infant sleep quality in the control group of 29.17%. From the t-test, the p value of 0.001 ˂ α (0.05) means that H0 is rejected and Ha is accepted, which means that there is an influence between the two variables.The conclusion is that there is an effect of giving rose aromatherapy on the sleepduration of infants aged 0-28 days. Therefore, it is suggested that this research can be an alternative to get good sleep quality for infants aged 0-28 days. Keywords : Infant, Rose Aromatherapy, Sleep Duration. Bibliography : (35) 2015-2021

When smoking is the smallest problem: A qualitative study of smoking prevention in Danish vocational schools

Objective: In Denmark, more students smoke in vocational schools than in upper secondary schools. Smoking-prevention initiatives may reduce the prevalence of smoking but may also introduce unforeseen challenges. This study examined the significance of smoking and dilemmas related to smokefree school hours (SFSH) among established smokers in Danish vocational schools. Design: Qualitative research design. Setting: Data collection took place in a vocational education and training (VET) school in Denmark. The subject area was care, health and pedagogy (CHP). Method: Participant observation and focus groups were conducted with students and teachers. Two classes were observed for 2 weeks in total. The data analysed consisted of participant observation field notes ( n = 10 school days), transcripts from student focus groups ( n = 6; aged 18–56) and transcripts from teacher focus groups ( n = 11). Results: Two dilemmas were described as faced by established smokers in relation to the implementation of smokefree school hours: (1) The school is entitled to make rules versus the right to decide for oneself; and (2) life and schoolwork are challenging and demanding versus school work is more important than smoking. Conclusion: The findings illustrate how smoking, as an ingrained everyday practice for established smokers, represents a means of coping with difficult school demands and everyday challenges, particularly for vulnerable students, and how regulations such as SFSH create dilemmas for those students. These dilemmas are important to consider when designing and implementing smoking-prevention initiatives to prevent potential negative side effects and an unintended increase in social inequality in smoking.

Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement

To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on multiscale feature fusion and a dual attention-augmented segmentation model (MFAM). The model uses the MobileViT network, which integrates a coordinate attention mechanism, as the feature extraction backbone network. It attains global and local expression capabilities through self-attention, class convolution, and coordinate attention and focuses on location information to expand the receptive field and achieve improved feature extraction efficiency. An A_R_ASPP feature enhancement module is proposed for the attention-optimized residual atrous spatial pyramid pooling module. The module uses residual connections to solve the small target loss problem in murals caused by the excessive sampling rate of atrous convolution and uses a feature attention mechanism to adaptively adjust the feature map weight according to the channel importance levels. A dual attention-enhanced feature fusion module is proposed for multiscale decoder feature fusion to improve the mural segmentation effect. This module uses a cross-level aggregation strategy and an attention mechanism to weight the importance of different feature levels to obtain multilevel semantic feature representations. The model improves the mean intersection over union (MIoU) by 3.06% and the MPA by 1.81% on a mural dataset compared with other models. The model is proven to be effective at improving the segmentation details, efficiency and small target segmentation results produced for mural images, and a new method is proposed for segmenting ancient mural images.

Harnessing machine learning and virtual sample generation for corrosion studies of 2-alkyl benzimidazole scaffold small dataset with an experimental validation

The present work deals with the development of a QSAR model based on Artificial Neural Network (ANN) algorithm for the prediction of inhibition efficiencies of 2-alkyl benzimidazole scaffold-based corrosion inhibitors for mild steel corrosion in 1 M HCl. The small dataset problems have been dealt with a Virtual Sample Generation (VSG) methodology using CTGAN algorithm, and credibility and generalizability of the proposed ANN+VSG model has been verified through experimental validation. Two new 2-alkyl benzimidazole scaffold-based corrosion inhibitor compounds namely EBIMOT and PBIMOT, were synthesised, their inhibition efficiencies were experimentally obtained, and the values showed a high resemblance with the predicted one by the model with good accuracy. Synthetic data embedding to the training samples enhances the model's recognition capacity of feature-target relationship and hence stabilizing and improving the correlation of the chemical quantum descriptors with the inhibition efficiency. This proposed method strengthens the prospect of ML for developing material designs, especially in the case of small datasets at a much cost-efficient, user-friendly, and accurate manner and can open doors to new and unexplored venues in the intersection of material science and computational intelligence.

Pre-Numerical Tests for a New Conjugate Gradient Method

The performance of a new beta in conjugate gradient method is generally measured on its CPU time and the number of iterations using large scale problems without concerning the accuracy of the solution attained. The method is claimed to be the best when less CPU time is used in comparison with the other classical beta. However, the accuracy of the solution gained compared to the exact solution of the function has never been tested as part of the performance measurement. Our previous research has already proven that our new beta perform well in both speed and number of iterations used. Therefore, in this research, we will accommodate this with alternative numerical test on the new beta in assessing the accuracy of the gained solution. This is to further ensure the efficiency of the new beta. Assessment was done with four other classical conjugate gradient methods on fourteen small scale problems. Results attained were promising to be tested for more tests in real functional problems.

Quantum computer based feature selection in machine learning

AbstractThe problem of selecting an appropriate number of features in supervised learning problems is investigated. Starting with common methods in machine learning, the feature selection task is treated as a quadratic unconstrained optimisation problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. The different results in small problem instances are compared. According to the results of the authors’ study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, the authors compare the convergence behaviour of the QUBO methods via quantum computing with classical stochastic optimisation methods. Due to persisting error rates, the classical stochastic optimisation methods are still superior.

Improving small sample medical image segmentation using CBAM: Insights from two datasets

Medical image segmentation is one of the key tasks in the medical field and is crucial for accurate lesion detection and treatment planning. However, the small sample problem has been one of the challenges in medical image segmentation. In this study, the small sample medical image segmentation problem is evaluated experimentally based on two different datasets, the VOC dataset and the esophageal medical images. In the VOC dataset experiments, we used the code from the BAM project on GitHub as a benchmark and compared it. Additionally, we improved the benchmark code by adding the Attention Mechanism (CBAM), placing its position before the support set undergoes global average pooling. By comparing the experimental results, we found that the model with the added CBAM achieved better segmentation results under small sample conditions. In the esophageal medical image experiments, the code from the BAM project was also used as a benchmark and a similar experimental design was performed. We further added CBAM by placing it before the global average pooling of the support set. The experimental results show that the model with the addition of CBAM achieves a significant improvement in the small sample segmentation task of esophageal medical images. To summarize, this study validates the effectiveness of using the BAM model in the small-sample medical image segmentation task on two different datasets. By adding CBAM, our model exhibits better segmentation performance under small sample conditions. These findings provide useful insights for small-sample medical image segmentation and are important for future research and practice.

In Vitro and in Silico Analysis of α -Amylase Inhibitory Activity of Ethanolic Extract of Adhatoda vasica Leaves.

Diabetic individuals have a higher probability of suffering from illness and death due to small blood vessel-related problems such as retinopathy, neuropathy, nephropathy, and stroke than other complications. There are many synthetic anti-diabetic agents available, but these can be expensive and have undesirable pathological effects. The enzyme α-amylase (hydrolase), catalyzes the hydrolysis of starch to maltose and glucose via the cleavage of α-1,4-glucosidic linkages. Diabetes mellitus patients may benefit from a therapeutic strategy that involves slowing the hydrolysis of starch by inhibiting the activity of α-amylase. Thus, looking for cost-effective, natural, and safe antidiabetic agents is essential. This study aims to screen phytoconstituents and evaluate the in-vitro and in-silico α-amylase inhibitory activity of the ethanolic extract of Adhatoda vasica leaves. The extraction of Adhatoda vasica leaves was performed using ethanol via the Soxhlet extraction process. Different concentrations (100μg/mL to 1000μg/mL) of ethanolic extract, Acarbose, and Sitagliptin, were prepared and evaluated for α-amylase inhibitory activity using the spectrophotometric method. Molecular docking (AutodockVina 1.2.0) and toxicity profiling (SToPToX web server) studies were performed. The ethanolic extract of Adhatoda vasica leaves showed the highest percentage inhibition against α-amylase (56.763 ± 0.0035) at a concentration of 1000μg/mL. The in-silico study supported this inhibitory activity. Vasicoline (C5) and Quercetin (C9), the active constitute of Adhatoda vasica, showed the best binding energies of -8.3 and -8.0Kcal/mol, respectively against α-amylase enzyme (PDBID: 4W93). A toxicity study revealed the safety profile of the plant extract. It was concluded that Adhatoda vasica leaves possess some bioactive compounds that are responsible for controlling blood glucose levels, and their identification, purification, and isolation may lead to the development of new therapeutic agents with fewer side effects than the available drugs.

No-Wait Open-Shop Scheduling Problems with Reverse Flows

Open shop scheduling has a special place in both production and optimization problems. This study investigates an assembly (direct flow) and disassembly (reverse flow) scheduling problem in an open shop environment where both are processed on the same machine, but only one is a no-wait operation. The objective is to minimize makespan of jobs on all machines. A mixed-integer linear program is developed. GAMS 25.1.1.2 is used to solve small size test problems. Differential Evolution (DE) Algorithm is applied to large-scale problems. A comparison of the computing results of this algorithm and the Genetic algorithm (GA), Whale Optimization Algorithm (WOA), Bat algorithm (BA), Artificial bee colony algorithm (ABC), and vibration damping optimization algorithm (VDO) is presented. Based on the results obtained, it has been found that there are no obvious differences between the performance of the algorithms, but the DE algorithm produces better solutions.

An explicit substructuring method for overlapping domain decomposition based on stochastic calculus

In a recent paper [7], a hybrid supercomputing algorithm for elliptic equations has been proposed. The idea is that the interfacial nodal solutions solve a linear system, whose coefficients are expectations of functionals of stochastic differential equations confined within patches of about subdomain size. Compared to standard substructuring techniques, such as the Schur complement method for the skeleton, the hybrid approach produces an explicit and sparse shrunken matrix—hence suitable for substructuring again. The ultimate goal is to push strong scalability beyond the state of the art by leveraging the potential for parallelisation of stochastic calculus. Here, we present a major revamping of that framework, based on the insight of embedding the domain in a cover of overlapping circles (in two dimensions). This allows for efficient Fourier interpolation along the interfaces (now circumferences) and—crucially—for the evaluation of most of the interfacial system entries as the solution of small boundary value problems on a circle. This is both extremely efficient (as they can be solved in parallel and by the pseudospectral method) and free of Monte Carlo error. Stochastic numerics are only needed on the relatively few circles intersecting the domain boundary. In sum, the new formulation is significantly faster, simpler, and more accurate while retaining all of the advantageous properties of PDDSparse. Numerical experiments are included for the purpose of illustration.

Bearing fault diagnosis via fusing small samples and training multi-state Siamese neural networks

Recently, deep learning techniques have been widely applied to fault diagnosis due to their outstanding feature extraction abilities. The success of deep-learning-based fault diagnosis methods is highly dependent on the quantity and quality of the training data. In practical scenarios, it is challenging to obtain sufficient high-quality training data for fault diagnosis tasks due to complex environments, which would affect the effectiveness of the deep learning methods. In this paper, a new fault diagnosis method is proposed for motor bearing fault diagnosis under small samples. The Siamese neural networks (SNNs) are employed to extract the fault features. A multi-stage training strategy is proposed to train the SNNs with the aim to prevent the training stagnation problem and handle the small sample problem. A multi-source feature fusion network is developed to make full use of the multi-source sensor data by fusing the extracted fault features for further fault diagnosis. The proposed method is applied to motor bearing fault diagnosis on two real-world datasets. Experimental results demonstrate the effectiveness and feasibility of the introduced method for motor bearing fault diagnosis under small samples.

An automatic online optics classification method used in the final optics recycling loop strategy

The final optics recycling loop used on large laser facility is an effcient and necessary strategy to maintain the optics in intervals between long-term physical experiments, In order to avoid cross-use between optics of different quality levels, we put forward the A/B recycling loop strategy of optics refer to the strategy from NIF, which arranges optics of the same quality level working in the same beamline, and optics of large quality difference loop independently to protect high-quality optics. Additionally, in order to improve the efficiency of recycling task and the stability of classification standard, we have searched for a proper intelligence technology applying on the loop process. To solve the small sample problem under each label, we use data augmentation method to manually increase samples. Furthermore, transfer learning method based on VGG16 is also used to extract features of the target. As a result the accuracy of the learning model reaches over 90 %. From now on, the integration and data interaction with the computer centralized control system have been completed. The realization of online image automatic classification can estimate the quality grades of optics, so as to provide reference for optics maintaining and optimize the efficiency of the optics recycling loop.

Impact of Digital Marketing on Micro, Small, and Medium Enterprises (MSMEs)

The micro, small, and medium enterprises (MSMEs) in India produce a varied collection of goods. The rapid changes in technology and consumer preferences have a huge impact on marketing techniques. In contrast to large industries, MSMEs do not have any proper tools for marketing so it is one of the weakest areas wherein small organizations face problems. This article explores the digital marketing prospects of MSMEs. These industries are major sources of employment, growth, and economy so digital marketing of SMEs is important. The various benefits of digital marketing with different tools available are highlighted in this article. This article will also help small industries in creating effecting websites so that they can take advantage of digital marketing. Web analytic tools for analysing website traffic are also discussed in this article so that companies can monitor the effect of digital marketing.

A novel approach for bearings multiclass fault diagnosis fusing multiscale deep convolution and hybrid attention networks

Insufficient and imbalanced samples pose a significant challenge in bearing fault diagnosis, leading to low diagnosis accuracy. However, the fault characteristics of vibration signals are weak and difficult to extract when faults occur in the early stage. This paper proposes an effective fault diagnosis method that addresses small and imbalanced sample problems under noise interference. First, the number of faulty samples in the form of 1D signals is increased mainly by the sliding split sampling method. The preprocessed data are used to create 2D time–frequency diagrams using the continuous wavelet transform (CWT), which can extract effective features to improve the data quality. Subsequently, the minority samples are oversampled by combining synthetic minority oversampling technique to realize time–frequency conversion augmented oversampling. Moreover, the clustering method and random undersampling method are introduced to prevent the overfitting and underfitting problems respectively. Then, we propose a hybrid attention mechanism to enhance the extraction of effective feature information. This combination, integrating CWT with a multicolumn modified deep residual network, effectively extracts fault characteristics and suppresses noise effects. The experimental results demonstrate the effectiveness of the proposed method by comparison with other advanced methods using two case studies of bearing datasets.

Investigation on the effect of wavelet mutation and opposition learning on slime mould algorithm

The study focuses on a hypothesis-based critical analysis of proposed modifications to the slime mould algorithm. The modifications being investigated are opposition learning and wavelet mutation. These modifications help the slime mould algorithm to avoid local optima for better exploration of the search space. The goal is to assess the effectiveness of these modifications in improving the performance of slime mould algorithm. Both, the basic slime mould algorithm, and the proposed variant eventually achieve the desired convergence. However, to compare the efficiency of the two algorithms, the study introduces a measurement index called the ‘swiftness’ of the algorithm. This index quantifies the speed at which an algorithm reaches convergence. It is calculated as the area under the convergence curve of each algorithm. The rationale behind this approach is that visual inspection alone may not be sufficient to discriminate between the algorithms based on the convergence curve. The hybrid approach, incorporating opposition learning and wavelet mutation, is evaluated statistically using the CEC 2008 benchmark function. Additionally, the study examines small and medium single-objective power dispatch optimization problems that adhere to non-convex system limitations. By assessing the performance of the modified slime mould algorithm on these different problem domains, the researchers aim to provide a comprehensive analysis of its effectiveness.

Relevansi Ilmu Ushul Fiqh di Era Kontemporer

Islamic law is used as a guide for Muslims in carrying out life, because Islam becomes a whole religion (kaffah) that regulates all aspects of life from small problems to big problems that exist according to the Qur'an and Sunnah. Shafi'iyah scholars explain that ushul fiqh is to look at the general legal basis by looking at its use as well as the person who performs ijtihad. This type of research uses literature research that takes data through information by obtaining library materials, data collection techniques for this research using documentation. data that has been obtained by reviewing and analyzing, both in the form of books, journals, research results. As well as by using descriptive analysis techniques. One of the uses of ushul fiqh is to provide legal basis capital by using the method carried out by mujtahid in providing new problem solutions in accordance with syar'i. The science of ushul fiqh continues to undergo renewal in the contemporary era because the problems are increasingly numerous and diverse, various theories are used to express opinions or laws to be determined, such as Fazlur Rahman through the theory of the Double Movement, Other thoughts related to the relevance of ushul fiqh science in the contemporary era are that there is a method formed by Abu Zayd with Ta'wil theory. In the book mafhum by Abu Zayd, in describing the invisible meaning in the text. In the sense that both implied and express meanings in the content of the law are discussed in the method from Abu Zayd.

A stable diffusion enhanced YOLOV5 model for metal stamped part defect detection based on improved network structure

Machine-vision-based defect detection for large metal stamping is a fundamental requirement for improving product quality and inspection speed. However, the performance of machine vision in detecting defects is limited by the large variety of stamped products, significant dimensional differences, and long data-acquisition cycles. To overcome these problems, metal stamping detection has been achieved using the improved YOLOV5 model, which features a slim neck and a multiheaded self-attentive mechanism for detecting wrinkles, holes, and cracks in metal stamping. An image-processing module was used to reduce the impact of metal reflections and improve image quality. Stable diffusion improvement was used to augment the dataset to overcome the small dataset size problem and enhance its generalisation capability. In addition, a BotNet structure with a self-attentive mechanism was introduced into the YOLOV5 model backbone to improve the image feature extraction capability. We then optimised the prediction head structure of YOLOV5 to improve the detection speed and accuracy. Ablation experiments were performed to analyse and verify the effectiveness of each module. The results of the ablation experiments show that the mAP of our proposed stable diffusion improvement data enhancement method, and the YOLO-Bot-VOV algorithm, for metal stamped part defect detection reached 98.2 %, and the parameters were reduced by 0.432 million.

Customized GRASP for rehabilitation therapy scheduling with appointment priorities and accounting for therapist satisfaction

Physical therapy in acute care hospitals plays an important role in the rehabilitation of patients. Nevertheless, the profession must deal with staff shortages caused by a lack of potential employees and absenteeism which are results of high physical and mental workloads. The therapist shortage negatively affects the total number of daily appointments the department can fulfill. For appointments that can be successfully scheduled, continuity of care with the same therapist cannot be guaranteed for individual patients. Lack of continuity of care negatively influences the therapist's satisfaction. Therapist preferences for individual appointments in general cannot always be guaranteed when designing schedules, which also hurts satisfaction. This paper develops a multi-criteria model for the daily therapy appointment-scheduling problem. The primary objective is to minimize the total sum of priority violations for unscheduled appointments. To improve therapist satisfaction, we consider therapist preferences including continuity of care as a secondary objective. Here, our integer programming formulation aims to minimize the total sum of preference violations for scheduled appointments. We are dealing with an operational planning problem with a daily planning horizon. The operational objective is to achieve therapist schedules in at most two hours. The therapists’ schedules together need to include several hundred appointments for a planning day. Due to intractability, the developed integer program cannot provide schedules for such problem sizes. Therefore, we develop a customized Greedy Randomized Adaptive Search Procedure (GRASP) with six innovative local search operations to improve an initially constructed solution. We test the heuristic algorithm on realistic data instances. The metaheuristic provides high-quality schedules for various problem sizes in short runtimes, i.e., within minutes. Comparisons with the optimal solutions for small problem instances show very good results of the GRASP with a similar number of scheduled appointments and good adherence to continuity of care and therapist preference requirements.

Accuracy Analysis for Explicit-Implicit Finite Volume Schemes on Cut Cell Meshes

The solution of time-dependent hyperbolic conservation laws on cut cell meshes causes the small cell problem: standard schemes are not stable on the arbitrarily small cut cells if an explicit time stepping scheme is used and the time step size is chosen based on the size of the background cells. In May and Berger (J Sci Comput 71: 919–943, 2017), the mixed explicit-implicit approach in general and MUSCL-Trap (monotonic upwind scheme for conservation laws and trapezoidal scheme) in particular have been introduced to solve this problem by using implicit time stepping on the cut cells. Theoretical and numerical results have indicated that this might lead to a loss in accuracy when switching between the explicit and implicit time stepping. In this contribution, we examine this in more detail and will prove in one dimension that the specific combination MUSCL-Trap of an explicit second-order and an implicit second-order scheme results in a fully second-order mixed scheme. As this result is unlikely to hold in two dimensions, we also introduce two new versions of mixed explicit-implicit schemes based on exchanging the explicit scheme. We present numerical tests in two dimensions where we compare the new versions with the original MUSCL-Trap scheme.

AnomalySeg: Deep Learning-Based Fast Anomaly Segmentation Approach for Surface Defect Detection

Product quality inspection is a crucial element of industrial manufacturing, yet flaws such as blemishes and stains frequently emerge after the product is completed. Most research has utilized detection models and avoided segmenting networks due to the unequal distribution of faulty information. To overcome this challenge, this work presents a rapid segmentation-based technique for surface defect detection. The proposed model is based on a modified U-Net, which introduces a hybrid residual module (SAFM), combining an improved spatial attention mechanism and a feedforward neural network in place of the remaining downsampling layers, except for the first layer of downsampling in the encoder, and applies this residual module to the decoder structure. Dilated convolutions are also incorporated in the decoder to obtain more spatial information about the feature defects and to reduce the gradient vanishing problem of the model. An improved hybrid loss function with Dice and focal loss is introduced to alleviate the small defect segmentation problem. Comparative experiments were conducted on different segmentation-based inspection methods, revealing that the Dice coefficient (DSC) evaluated by the proposed approach is better than previous generic segmentation benchmarks on KolektorSDD, KolektorSDD2, and RSDD datasets, with fewer parameters and FLOPs. Additionally, the detection network displays higher precision in recognizing the characteristics of minor flaws. This paper proposes a practical and effective technique for anomaly segmentation in surface defect identification, delivering considerable improvements over previous methods.