Matching Stage Research Articles

Business Development Strategy for Chocolate Rangginang Products at Kang Asrok’s Micro Small Business in Banjar City West Java Province

Kang Asrok is a small micro-business engaged in the food processing industry, namely rangicok, which uses rangginang as a basic ingredient that is innovated with various flavors to become today's food that maintains the existence of traditional food. This study aims to analyze internal and external environmental factors: weaknesses, strengths, threats, and opportunities, formulate strategic alternatives and formulate business strategy priorities that can be a recommendation for Kang Asrok. This study uses primary data from the questionnaire of 3 experts, observation and interview with the owner, and an online survey of 20 respondents. This study uses a three-stage analysis consisting of the input stage, the matching stage, and the decision stage. This study was conducted using analysis tools such as the SWOT matrix, IFE, and EFE matrix, IE matrix, and QSP matrix. The results showed that Kang Asrok was in quadrant IV, which means the right strategy is growth and build. Based on the QSP matrix, the right strategy to be applied is market penetration to create brand awareness of the community towards the product got a STAS score of 5,30. Keywords: processing industry, QSPM, traditional food, SWOT

Local Election During the Covid-19 Outbreak: Inclusivity and Challenges for Voters with Disabilities

This study discusses the inclusiveness and challenges of voters with disabilities in the implementation of local election (Pilkada) during the Covid-19 outbreak. There are two things that need to be considered when implementing the 2020 local election, namely community participation, especially persons with disabilities and the application of health protocols. This study uses a qualitative research method with case study and a virtual ethnographic approach. The author focuses on the implementation of the 2020 local election in Sleman regency. In addition, the authors focus on three stages of local election, namely the stage of voter list matching and research (Coklit), campaign and socialization, and voting day. The results of the study show that political rights for voters with disabilities are regulated in several regulations, both at the international and national level. In addition, the General Election Commission (KPU) of Sleman Regency has carried out several preparations in the implementation of the 2020 local election during Covid-19 outbreak, such as conducting rapid tests for ad hoc organizers, conducting socialization and discussions with voters with disabilities, preparing health protocols at each stage, up to ensuring polling stations are accessible and inclusive. However, voters with disabilities experience several challenges based on the variety of diffabilities in each stage. The author also found several challenges experienced by voters with disabilities in accordance with the variety of disabilities, both motivated by Covid-19 outbreak, as well as challenges that have often occurred from local election to local election. It is hoped that all stakeholders involved, especially in the Local Election, can have an inclusive perspective, so that the Local Election is more friendly to voters with disabilities.

Dynamic pricing and penalty strategies in a coupled market with ridesourcing service and taxi considering time-dependent order cancellation behaviour

Time-dependent order cancellation behaviour affects ridesourcing platform operation and overall system performance. This paper models impacts of time-dependent order cancellation behaviour on the driver–rider matching efficiency and examines ridesourcing platform operation strategies, where taxi is the alternative to the ridesourcing mode. A time-dependent matching model is developed to characterize the complex interaction among participants (drivers and riders) considering ridesourcing order cancellation behaviour, where the impact of the cancelled orders on the numbers of drivers and riders is explicitly modelled. In particular, we formulate the time-dependent order cancellation rates before and after the matching stage (i.e., cancellation of unconfirmed and confirmed orders). Under the proposed model, a platform profit maximization problem is formulated and three pricing strategies are examined. Our numerical studies demonstrate that the dynamic pricing (i.e., customer/rider fare and driver wage) can well accommodate time-dependent system inputs (e.g., demand rates) and thus enable the platform to increase profit via better market segmentation. We also investigate the objective of maximizing the number of completed trips and examine the trade-off between the platform profit and the number of completed trips. In addition, we show that the relaxation of upper bounds of the ridesourcing fare and order cancellation penalty can increase the ridesourcing platform’s profit and indirectly improve the utilization rate of taxis as well as the taxi company’s profit.

Fingerprint recognition based on collected images using deep learning technology

The fingerprint identification is the most widely used authentication system. The fingerprint uniqueness for each human being provides error-free identification. However, during the scanning process of the fingerprint, the generated image using the fingerprint scanner may differ slightly during each scan. This paper proposes an efficient matching model for fingerprint authentication using deep learning based deep convolutional neural network (CNN or ConvNet). The proposed deep CNN consists of fifteen layers and is classified into two stages. The first stage is preparation stage which includes the fingerprint images collection, augmentation and pre-processing steps, while the second stage is the features extraction and matching stage. Regarding the implantation results, the proposed system provided the best matching for the given fingerprint features. The obtained training accuracy of the proposed model is 100% for training dataset and 100% for validating dataset.

Finite Element Analysis of Femoral-Acetabular Impingement (FAI) Based on Three-Dimensional Reconstruction.

In order to solve the problem that people often have pain in the hip joint, it is more meaningful to study femoral-acetabular impingement syndrome in the future. This article aims to study the finite element analysis of femoral-acetabular impingement based on three-dimensional reconstruction. This paper proposes a selective image matching strategy. In the feature matching stage, all images are not matched in pairs, but the corresponding camera distance between the images is calculated initially, which has little effect on the number of features and greatly reduces the time of feature matching, thereby reducing the time cost of 3D reconstruction. In this experiment, a double-blind experiment was used to check the range of motion of all hip joints. Two senior radiologists read the obtained hip joint orthographic films to screen out the hip joint orthographic films that meet the requirements. Experimental data shows that although the initial matching points of the algorithm in this paper are lower than those of the traditional algorithm, the final number of matching points is higher than that of the traditional algorithm. When the final number of patches is fixed to 10000, the initial patch required by the algorithm in this paper is more than that required by the SAD algorithm, nearly 13%, but the total storage requirement is 56.4% of the SAD algorithm, which is a big improvement.

THE STRENGTHENING MARKETING STRATEGY OF THE PEARL JEWELRY CRAFTS INDUSTRY IN MATARAM DUE TO THE IMPACT OF COVID 19 PANDEMIC

The purpose of research conducted on the creative industry of pearl jewelry is to analyze the strengthening of industrial marketing strategy due to the impact of covid-19 pandemic in Mataram. The type of research used is qualitative with a descriptive type of approach. Analysis is carried out in three stages, namely: the first stage as a data input stage on the IFE and EFE matrices, the second stage as a matching stage using the SWOT matrix, and the third stage was the formulation of strengthening marketing strategies for the pearl jewelry industry. The results show that this industry actually has a strong foundation to develop in future. Industry capacity and competence have been built for a long time as a legacy from previous generations, pearl quality, and the industry's ability to build networks with partners and consumers outside the region and even abroad can be a source of competitive advantage for the industry going forward. The Covid pandemic as the most dominant threat variable has collapsed the industrial structure, sales have fallen sharply, production activities and supply of raw materials have stopped. Most of the business actors in all production and marketing chains are unemployed. Strengthening the marketing strategy is formulated so that the industry can get out of unusual situations, namely the industry must build core competencies, using social media in marketing, using market places, continuing to build positive perceptions and attitudes about product quality, increasng craftsman creativity in design skills, and providing training to entrepreneurs and merchants on online marketing. Keywords: strategy, marketing, craft, pearl

3MRS: An Effective Coarse-to-Fine Matching Method for Multimodal Remote Sensing Imagery

The fusion of image data from multiple sensors is crucial for many applications. However, there are significant nonlinear intensity deformations between images from different kinds of sensors, leading to matching failure. To address this need, this paper proposes an effective coarse-to-fine matching method for multimodal remote sensing images (3MRS). In the coarse matching stage, feature points are first detected on a maximum moment map calculated with a phase congruency model. Then, feature description is conducted using an index map constructed by finding the index of the maximum value in all orientations of convolved images obtained using a set of log-Gabor filters. At last, several matches are built through image matching and outlier removal, which can be used to estimate a reliable affine transformation model between the images. In the stage of fine matching, we develop a novel template matching method based on the log-Gabor convolution image sequence and match the template features with a 3D phase correlation matching strategy, given that the initial correspondences are achieved with the estimated transformation. Results show that compared with SIFT, and three state-of-the-art methods designed for multimodal image matching, PSO-SIFT, HAPCG, and RIFT, only 3MRS successfully matched all six types of multimodal remote sensing image pairs: optical–optical, optical–infrared, optical–depth, optical–map, optical–SAR, and day–night, with each including ten different image pairs. On average, the number of correct matches (NCM) of 3MRS was 164.47, 123.91, 4.88, and 4.33 times that of SIFT, PSO-SIFT, HAPCG, and RIFT for the successfully matched image pairs of each method. In terms of accuracy, the root-mean-square error of correct matches for 3MRS, SIFT, PSO-SIFT, HAPCG, and RIFT are 1.47, 1.98, 1.79, 2.83, and 2.45 pixels, respectively, revealing that 3MRS got the highest accuracy. Even though the total running time of 3MRS was the longest, the efficiency for obtaining one correct match is the highest considering the most significant number of matches. The source code of 3MRS and the experimental datasets and detailed results are publicly available.

Biology and Clinical Implications of Fecal Occult Blood Test Screen-Detected Colorectal Cancer.

BackgroundFecal occult blood test (FOBT)–based screening for colorectal cancer (CRC) reduces mortality, with earlier stage at diagnosis a prominent feature. Other characteristics of FOBT screen-detected cancers and any implications for clinical management have not been well explored.MethodsWe examined a multisite clinical registry to compare the characteristics and outcomes of FOBT screen-detected CRC via the Australian National Bowel Cancer Screening Program (NBCSP), which is offered biennially to individuals aged 50-74 years, and age-matched non-screen-detected CRC in the same registry. All statistical tests were 2-sided. Odds ratios (ORs) were calculated using the Baptista-Pike method, and hazard ratios via the log-rank method.ResultsOf 7153 registry patients diagnosed June 1, 2006, to June 30, 2020, 4142 (57.9%) were aged between 50 and 74 years. Excluding 406 patients with non-NBCSP screen-detected cancers and 35 patients with unknown method of detection, 473 (12.8%) were screen detected via the NBCSP, and 3228 (87.2%) were non-screen detected. Screen-detected patients were younger (mean age = 62.4 vs 64.2 years; P < .001) and more medically fit (OR for ASA score 1-2 = 1.91, 95% confidence interval [CI] = 1.51 to 2.41; P < .001). Pathologic characteristics within each stage favored the screen-detected patients. Stage III screen-detected colon cancers were more likely to receive adjuvant therapy (OR = 3.58, 95% CI = 1.52 to 8.36; P = .002). Screen-detected patients had superior relapse-free (hazard ratio = 0.41, 95% CI = 0.29 to 0.60; P < .001) and overall survival (hazard ratio = 0.22, 95% CI = 0.15 to 0.35; P < .001), which was maintained in matched stage comparisons and multivariable analysis.ConclusionsBeyond stage at diagnosis, multiple other factors associated with a favorable outcome are observed in FOBT screen-detected CRC. Given the substantial stage-by-stage differences in survival outcomes, if independently confirmed, individualized adjuvant therapy and surveillance strategies could be warranted for FOBT screen-detected cancers.

Effect of the Matching Window Size and TDI Stage Number on Image-Based Satellite Jitter Detection

For time-delay integration (TDI) linear push-broom satellite images, it is generally considered that the matching window size and TDI stage number affect the jitter detection capability, but there are very few quantitative studies. In this letter, a scheme for investigating the effect of the matching window size and TDI stage number on image-based jitter detection is designed, and the model of image motion caused by jitter and the model of jitter detection and estimation for TDI images are deduced and established. Through a comprehensive experimental analysis, it is revealed that the influence of the TDI stage number and matching window size on jitter frequency detection is not significant, which improves our previous understanding. Although the TDI mode will attenuate the jitter amplitude by integration, the real jitter amplitude can be accurately retrieved by the proposed estimation model with attenuation compensation. The matching window size in the row direction has a significant effect on jitter amplitude detection. To ensure the detection accuracy of the jitter amplitude, the matching window size in the row direction is suggested to be less than 1/5 of the image line count corresponding to a jitter cycle.

Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching

Iris is one of the most accurate biometrics. This has led to the successful development of large-scale applications. However, with population growth, and new international applications, datasets are constantly increasing in size, requiring more robust and faster methods. Many descriptors and feature extractors have been developed to extract features that represent the iris biometric pattern. Most of them have been designed by human experts and require a bit-shifting process to increase their robustness to eye rotations, at the expense of increased matching time. We propose a fast iris recognition method that requires a single matching operation and is based on pre-trained image classification models as feature extractors. Our approach uses the filters of the first layers from Convolutional Neural Networks as feature extractors and does not require fine-tuning for new datasets. Since our selected features extracted from convolutional layers encode the iris surface, they have the advantage of not being restricted to specific spatial positions. Thus, it is not necessary to perform a bit-shifting process in the matching stage, eliminating a significant number of computations. Additionally, to mitigate the effect produced by the mask border in rubber-sheet images, we propose filtering the feature map tensors by masking their channels and selecting the most relevant features. Our method was assessed on the publicly available datasets CASIA Iris Lamp and CASIA Iris Thousand, and showed significant improvement both in accuracy and in matching time.

Three-Dimensional Video Super-Resolution Reconstruction Scheme Based on Histogram Matching and Recursive Bayesian Algorithms

Multimedia Super-Resolution (SR) reconstruction is an essential and mandatory process for different visualization functions. Recently, several schemes have been suggested for single- and multi-image SR reconstruction. This work presents an effective SR reconstruction scheme for visual quality and resolution enhancement of 3D Video (3DV) sequences. The idea behind the proposed 3DV SR reconstruction scheme is the utilization of a recursive Bayesian algorithm for improving the resolution of the degraded 3DV sequences with down-sampling, blurring, and noise effects. In addition, a significant stage of histogram matching based on a visual image with a better-distributed histogram is employed. The main aim of employing the histogram matching stage for enhancing the 3DV sequence is to introduce a dynamic range modification of each 3DV frame. Hence, it presents a 3DV sequence with an enhanced distribution of intensities. This improves the whole performance efficiency of the suggested scheme. The performance of the proposed SR reconstruction scheme is compared with that of the conventional bicubic interpolation scheme. Comparisons with recent and related SR reconstruction schemes are also introduced. Simulation results reveal that the proposed scheme achieves superior outcomes in terms of Structural Similarity (SSIM) index, local contrast, average gradient, Mean Square Error (MSE), edge intensity, entropy, and Peak Signal-to-Noise Ratio (PSNR) of the resulting 3DV frames.

A new function of stereo matching algorithm based on hybrid convolutional neural network

This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.

Image and model sequences matching for on-site assembly stage identification

In an assembly task, stage identification determines the subsequent parts to be assembled and therefore, it affects the quality and efficiency of the entire assembly operation in computer-aided manufacturing. However, due to the similarity between the assemblies in two adjacent stages, it is difficult to distinguish the assembly accurately based on the current visual recognition method. Considering the time and progress logic of assembly procedures, a stage identification method based on the on-site assembly sequence and stage model sequence is proposed. The on-site assembly sequence is generated with the assembly progress to record the history information in the time order and the stage model sequence is preprocessed in the database in the progress order. On the one hand, these two sequences provide the constraints to avoid the inefficient and indistinguishable assembly images to be analyzed. On the other hand, the two sequences contain time and assembly progress logic, which can be used to solve the stage identification issue using an optimal mapping model, so as to improve the identification efficiency. This paper introduces the methodology from three aspects, namely, offline stage model sequence generation, online assembly sequence matching and assembly stage identification. The experiments proved the proposed method can effectively improve the accuracy of real-time assembly stage identification compared with the original object recognition method.

Variables affecting outcomes after allogeneic hematopoietic stem cell transplant for cerebral adrenoleukodystrophy

AbstractAllogeneic hematopoietic stem cell transplantation (allo-HSCT) in early cerebral adrenoleukodystrophy can stabilize neurologic function and improve survival but has associated risks including transplant-related mortality (TRM), graft failure, and graft-versus-host disease (GVHD). An observational study of 59 patients with median age at allo-HSCT of 8 years addressed impact of donor source, donor match, conditioning regimen, and cerebral disease stage on first allo-HSCT outcomes. Efficacy analyses included 53 patients stratified by disease category: advanced disease (AD; n = 16) with Loes score >9 or neurological function score (NFS) >1 and 2 early disease (ED) cohorts (ED1 [Loes ≤4 and NFS ≤1; n = 24] and ED2 [Loes >4-9 and NFS ≤1; n = 13]). Survival free of major functional disabilities and without second allo-HSCT at 4 years was significantly higher in the ED (66%) vs AD (41%) cohort (P = .015) and comparable between ED1 and ED2 cohorts (P = .991). The stabilization of neurologic function posttransplant was greater in the ED vs AD cohort, with a median change from baseline at 24 months after allo-HSCT in NFS and Loes score, respectively, of 0 and 0.5 in ED1 (n = 13), 0.5 and 0 in ED2 (n = 6), and 2.5 and 3.0 (n = 4) in AD cohort. TRM was lower in the ED (7%) compared with the AD (22%) cohort; however, the difference was not significant (P = .094). Transplant-related safety outcomes were also affected by transplant-related characteristics: graft failure incidence was significantly higher with unrelated umbilical cord grafts vs matched related donors (P = .039), and acute GVHD and graft failure incidences varied by conditioning regimen. This study was registered at www://clinicaltrials.gov as #NCT02204904.

An ear recognition system based on local wavelet subband energy distribution

The outer ear features have been used for many years in forensic science of recognition. Human ear is a valuable information provenance of data for individual identification/authentication. Ear meets biometric characteristic (universality, distinctiveness, permanence and collectability). Biometric system depending on ear image facing two major challenges; the first one is the localization of human ear area in given profile face image, and the second one is the selection of proper features to distinguish between individuals. In this work, we propose an alogorithm for ear recognition based on the local spatial energy distribution of wavelet sub-bands, because of wavelet transform has the ability to analyze the local feature of 2-D image by determining where the low frequency and high frequency areas are and it provides full description of the spatial distribution of the ear image. Nearest classifier are used to make a recognition decision in matching stage. The system was tested over a public database consist of 493 images. The attained recognition rate was (95.28%) and the achieved minimum equal error rate (EER) is 0.02%.

Formulation Stategies for Vocational High School in Surabaya ( Case Study: SMKS “X”)

This research focuses on strategic planning conducted at one of the private vocational high schools in Surabaya. Vocational school is one of the services in the education sector that aims to prepare students to work in specific fields. At this time, the number of students enrolling has decreased, so the need for proper strategic planning for schools in increasing the number of students. The method used in this study is the formulation of internal and external factors using the IFE and EFE matrices as the input stage and followed by determining the weights for each factor using pair comparison. In the next stage, this study uses the SWOT matrix as a strategy matching stage. The results showed the value of the IFE matrix. Schools had a total rating of 2.5, while the scores on the EFE matrix, schools had a total rating of 2.878. Based on an analysis of internal and external factors using the SWOT matrix, several alternative strategies can be obtained including Development of New Departments; Establish relationships with junior high schools in the same institution; Development of Learning Processes; Increased School Promotion; Maximizing BOS Funds; Collaboration with DU / DI; School Resource Development; School Culture Development and School Program Improvement; Improved School Performance and Quality. For the design of the strategy map obtained from four perspectives, including: (a) Financial Perspectives: Regular Provision of Budgets, Income Levels, Utilization of BOS Funds and School Imagery; (b) Customer Perspective: Number of Students, Customer Satisfaction, and School Quality; (c) Internal Business Perspectives: Marketing Development, Innovation, Process, After Sales Service, and Relationship Enhancement with DU / DI; (d) Learning and Growth Perspectives: Teacher / Staff Satisfaction, School Financial Management Development, Information System Capability, Teacher / Staff Development, and School Culture Development.

A descriptive analysis of non-human leukocyte antigens present in renal transplant donor-recipient pairs

IntroductionEnd stage renal disease (ESRD) is the irreversible deterioration of renal function requiring renal replacement therapy by dialysis or transplant. Human leucocyte antigens (HLA) have been well examined however research still is required into the non-HLA antibodies. Antibody mediated rejection (AMR) can be seen in the absence of HLA antibodies on biopsies of patients who have received identical transplants; anti-endothelial cell antibodies may explain this. Investigation into endothelial cell antigens on donor and recipient endothelium may elucidate and stratify the degree of risk of any given transplant and may guide towards the best matched donor. MethodsProtein array analysis was carried out on 8 patient pairs using nitro-cellulose membranes and biotinylated detection antibodies. The fluorescence emitted was captured by X-Ray film and results were recorded with ImageJ software. A fold increase of more than 2 was considered to be positive. Results11 proteins identified had a fold increase of increase ≥2 and were present in ≥2 patient pairs which may point to potential clinical utility. Nectin2/CD112 may be measured in order analyse graft survival time in transplant recipients. Prognosticating renal failure has clinical importance and potential markers that have been identified to aid which include MEPE, CRELD2, and TIMP-4. Novel pharmacological therapies for specific biomarkers identified in this study include JAM-A, E-Selectin, CD147, Galectin-3, JAM-C, PAR-1, and TNFR2. ConclusionProtein analysis showed differences in expression of antigens between patients with and without Chronic Kidney Disease (CKD). This information could be used at the matching stage of renal transplantation and also in the treatment of rejection episodes. The results highlight biomarkers that potentially prognosticate and pharmacological therapies that may ameliorate kidney disease and rejection in ESRD and transplant recipients.

Nutritional Immunity, Zinc Sufficiency, and COVID-19 Mortality in Socially Similar European Populations.

The impact of zinc (Zn) sufficiency/supplementation on COVID-19-associated mortality and incidence (SARS-CoV-2 infections) remains unknown. During an infection, the levels of free Zn are reduced as part of “nutritional immunity” to limit the growth and replication of pathogen and the ensuing inflammatory damage. Considering its key role in immune competency and frequently recorded deficiency in large sections of different populations, Zn has been prescribed for both prophylactic and therapeutic purposes in COVID-19 without any corroborating evidence for its protective role. Multiple trials are underway evaluating the effect of Zn supplementation on COVID-19 outcome in patients getting standard of care treatment. However, the trial designs presumably lack the power to identify negative effects of Zn supplementation, especially in the vulnerable groups of elderly and patients with comorbidities (contributing 9 out of 10 deaths; up to >8,000-fold higher mortality). In this study, we have analyzed COVID-19 mortality and incidence (case) data from 23 socially similar European populations with comparable confounders (population: 522.47 million; experiencing up to >150-fold difference in death rates) and at the matching stage of the pandemic (March 12 to June 26, 2020; first wave of COVID-19 incidence and mortality). Our results suggest a positive correlation between populations’ Zn-sufficiency status and COVID-19 mortality [r (23): 0.7893–0.6849, p-value < 0.0003] as well as incidence [r (23):0.8084–0.5658; p-value < 0.005]. The observed association is contrary to what would be expected if Zn sufficiency was protective in COVID-19. Thus, controlled trials or retrospective analyses of the adverse event patients’ data should be undertaken to correctly guide the practice of Zn supplementation in COVID-19.

Efficient common objects localization based on deep hybrid Siamese network

Finding the common objects in a set of images is considered one of the recent challenges in different computer vision tasks. Most of the conventional methods have proposed unsupervised and weakly supervised co-localization methods to find the common objects; however, these methods require producing a huge amount of region proposals. This paper tackles this problem by exploiting supervised learning benefits to localize the common object in a set of unlabeled images containing multiple objects or with no common objects. Two stages are proposed to localize the common objects: the candidate box generation stage and the matching and clustering stage. In the candidate box generation stage, the objects are localized and surrounded by the bounding boxes. The matching and clustering stage is applied on the generated bounding boxes and creates a distance matrix based on a trained Siamese network to reflect the matching percentage. Hierarchical clustering uses the generated distance matrix to find the common objects and create clusters for each one. The proposed method is trained on PASCAL VOC 2007 dataset; on the other hand, it is assessed by applying different experiments on PASCAL VOC 2007 6×2 and Object Discovery datasets, respectively. The results reveal that the proposed method outperforms the conventional methods by 8% to 40% in terms of corloc metric.

A Prospective Study of the Effect of Tinnitus Sound Matching Degree on the Efficacy of Customized Sound Therapy in Patients with Chronic Tinnitus

Objectives: The aim of this study was to explore and compare the customized sound therapy effect between tinnitus sound matching and nonmatching patients in tinnitus customized sound therapy and therapy-related influencing factors. Methods: This prospective study investigated a total of 100 patients with unilateral chronic tinnitus who received customized sound therapy. The participants were dichotomously divided into matching (group A) and nonmatching (group B) groups after 4 stages of tinnitus matching via the tinnitus assistant app (provided by Sound Ocean Company, SuZhou, China). Each group consists of 50 participants. Before and 6 months after the treatment, Hospital Anxiety and Depression Scale (HADS), tinnitus handicap inventory (THI), and tinnitus loudness Visual Analog Scale (VAS) were used to evaluate the customized sound therapy effect and explore other related influencing factors. Results: (1) The HADS-A, HADS-D, THI, and VAS scores of 2 groups were both significantly decreased after treatment. (2) The HADS-A and THI scores improved markedly in group A than that in group B, which could be related to the hearing loss of the tinnitus side ear before treatment; the lighter the degree of hearing loss, the better the improvement. No statistically significant differences were detected in HADS-D and VAS scores between the 2 groups, and also, these were not related to the degree of hearing loss. The differences in age, gender, and tinnitus duration did not show any statistically significant effect on the improvement of the 2 groups. Conclusions: Both tinnitus sound matching and nonmatching of the customized sound therapy brought a significant effect to tinnitus participants. Our study also suggests that THI and HADS-A scores of those with tinnitus matching participants improved markedly as compared to those of nonmatching participants, and the customized sound therapy effect is negatively correlated with the severity of hearing loss.