Distributional Characteristics Research Articles

Self-Reported Functional Vision in USH2A-Associated Retinal Degeneration as Measured by the Michigan Retinal Degeneration Questionnaire.

The purpose of this study was to evaluate self-reported functional vision (FV) and the impact of vision loss in patients with USH2A-associated retinal degeneration using a patient-reported outcome (PRO) measure, the Michigan Retinal Degeneration Questionnaire (MRDQ), to correlate MRDQ scores with well-established visual function measurements. An observational cross-sectional study (n = 93) of participants who had Usher Syndrome Type 2 (USH2, n = 55) or autosomal recessive non-syndromic retinitis pigmentosa (ARRP; n = 38) associated with biallelic variants in the USH2A gene. The study protocol was approved by all ethics boards and informed consent was obtained from each participant. Participants completed the MRDQ at the 48-month study follow-up visit. Disease duration was self-reported by participants. One-way ANOVA was used to compare subgroups (clinical diagnosis, age, disease duration, and full-field stimulus threshold [FST] Blue-Red mediation) on mean scores per domain. Spearman correlation coefficients were used to assess associations between MRDQ domains and visual/retinal function assessments. Of the study sample, 58% were female participants and the median disease duration was 13years. MRDQ domains were sensitive to differences between subgroups of clinical diagnosis, age, disease duration, and FST Blue-Red mediation. MRDQ domains correlated with static perimetry, microperimetry, full-field stimulus testing, and best-corrected visual acuity (BCVA). Self-reported FV measured by the MRDQ, when applied to USH2 and ARRP participants, had good distributional characteristics and correlated well with visual function tests. MRDQ adds a new dimension of understanding on vision-related functioning and establishes this PRO tool as an informative measure in evaluating USH2A outcomes.

MediDRNet: Tackling category imbalance in diabetic retinopathy classification with dual-branch learning and prototypical contrastive learning

Background and Objective:The classification of diabetic retinopathy (DR) aims to utilize the implicit information in images for early diagnosis, to prevent and mitigate the further worsening of the condition. However, existing methods are often limited by the need to operate within large, annotated datasets to show significant advantages. Additionally, the number of samples for different categories within the dataset needs to be evenly distributed, because the characteristic of sample imbalance distribution can lead to an excessive focus on high-frequency disease categories, while neglecting the less common but equally important disease categories. Therefore, there is an urgent need to develop a new classification method that can effectively alleviate the issue of sample distribution imbalance, thereby enhancing the accuracy of diabetic retinopathy classification. Methods:In this work, we propose MediDRNet, a dual-branch network model based on prototypical contrastive learning. This model adopts prototype contrastive learning, creating prototypes for different levels of lesions, ensuring they represent the core features of each lesion level. It classifies by comparing the similarity between data points and their category prototypes. Our dual-branch network structure effectively resolves the issue of category imbalance and improves classification accuracy by emphasizing subtle differences in retinal lesions. Moreover, our approach combines a dual-branch network with specific lesion-level prototypes for core feature representation and incorporates the convolutional block attention module for enhanced lesion feature identification. Results:Our experiments using both the Kaggle and UWF classification datasets have demonstrated that MediDRNet exhibits exceptional performance compared to other advanced models in the industry, especially on the UWF DR classification dataset where it achieved state-of-the-art performance across all metrics. On the Kaggle DR classification dataset, it achieved the highest average classification accuracy (0.6327) and Macro-F1 score (0.6361). Particularly in the classification tasks for minority categories of diabetic retinopathy on the Kaggle dataset (Grades 1, 2, 3, and 4), the model reached high classification accuracies of 58.08%, 55.32%, 69.73%, and 90.21%, respectively. In the ablation study, the MediDRNet model proved to be more effective in feature extraction from diabetic retinal fundus images compared to other feature extraction methods. Conclusions:This study employed prototype contrastive learning and bidirectional branch learning strategies, successfully constructing a grading system for diabetic retinopathy lesions within imbalanced diabetic retinopathy datasets. Through a dual-branch network, the feature learning branch effectively facilitated a smooth transition of features from the grading network to the classification learning branch, accurately identifying minority sample categories. This method not only effectively resolved the issue of sample imbalance but also provided strong support for the precise grading and early diagnosis of diabetic retinopathy in clinical applications, showcasing exceptional performance in handling complex diabetic retinopathy datasets. Moreover, this research significantly improved the efficiency of prevention and management of disease progression in diabetic retinopathy patients within medical practice. We encourage the use and modification of our code, which is publicly accessible on GitHub: https://github.com/ReinforceLove/MediDRNet.

A Comprehensive Review of Chemical Looping Ammonia Synthesis

Ammonia, being a vital chemical compound, has made substantial contributions to the progress of society. It addresses not only the issue of nitrogen fixation from the atmosphere but also helps alleviate the food crisis. Nevertheless, the conventional Haber-Bosch process for synthesizing ammonia is frequently associated with substantial greenhouse gas emissions and the consumption of fossil fuels. Consequently, in response to significant environmental challenges, the prospective trajectory for the synthetic ammonia sector involves the adoption of strategies such as carbon reduction, energy-efficient conversions, and the exploration of environmentally friendly and sustainable approaches to ammonia synthesis. Among the various methods for green ammonia synthesis, chemical looping ammonia synthesis (CLAS) technology is regarded as one of the most promising alternatives to conventional ammonia synthesis methods in the upcoming years. This can be attributed to the relatively mild operating conditions and the flexible and distributed characteristics of its processes. In this paper, the thermodynamic and kinetic properties of nitridation and ammoniation reactions were examined through the lens of CLAS. The design and regulation strategies of nitrogen carriers (NCs) are summarized based on various types of NCs. The process of CLAS was summarized in relation to various NCs. This paper serves as a foundational resource for the prospective advancement of the CLAS framework.

Connectivity-Based Clustering for Mixed Discrete and Continuous Data

This paper introduces a density-based clustering procedure for datasets with variables of mixed type. The proposed procedure, which is closely related to the concept of shared neighbourhoods, works particularly well in cases where the individual clusters differ greatly in terms of the average pairwise distance of the associated objects. Using a number of concrete examples, it is shown that the proposed clustering algorithm succeeds in allowing the identification of subgroups of objects with statistically significant distributional characteristics.

Examining the Distributional Characteristics of Daily Returns of Nifty 50: Normality Assessment and Implications

Examining the Distributional Characteristics of Daily Returns of Nifty 50: Normality Assessment and Implications

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

The segmentation of crops and weeds from camera-captured images is a demanding research area for advancing agricultural and smart farming systems. Previously, the segmentation of crops and weeds was conducted within a homogeneous data environment where training and testing data were from the same database. However, in the real-world application of advancing agricultural and smart farming systems, it is often the case of a heterogeneous data environment where a system trained with one database should be used for testing with a different database without additional training. This study pioneers the use of heterogeneous data for crop and weed segmentation, addressing the issue of degraded accuracy. Through adjusting the mean and standard deviation, we minimize the variability in pixel value and contrast, enhancing segmentation robustness. Unlike previous methods relying on extensive training data, our approach achieves real-world applicability with just one training sample for deep learning-based semantic segmentation. Moreover, we seamlessly integrated a method for estimating fractal dimensions into our system, incorporating it as an end-to-end task to provide important information on the distributional characteristics of crops and weeds. We evaluated our framework using the BoniRob dataset and the CWFID. When trained with the BoniRob dataset and tested with the CWFID, we obtained a mean intersection of union (mIoU) of 62% and an F1-score of 75.2%. Furthermore, when trained with the CWFID and tested with the BoniRob dataset, we obtained an mIoU of 63.7% and an F1-score of 74.3%. We confirmed that these values are higher than those obtained by state-of-the-art methods.

Distributional Characteristics Analysis of Allergens in Patients with Allergic Rhinitis in Southern Fujian Province, China.

Allergic rhinitis (AR) is a chronic inflammatory disease of the nasal mucosa. However, few studies focus on the distributional characteristics of allergens in AR patients in Southern Fujian Province, China. A skin prick test (SPT) was performed and eight common allergens including Dermatophagoides farinae (Df), Dermatophagoides pteronyssinus (Dpt), weeds, animal dander, molds, cockroaches, and mangoes were chosen. The positive reactions rate to the allergens was 65.79% in 6689 patients in Southern Fujian Province. Positive reactions to Df and Dpt had a negative association with age, whereas positive reactions to cockroach and weed had a positive association with age. A linear trend analysis demonstrated a significant positive relationship between positive reactions to various allergens from 2016 to 2019. Positive reactions to Df and Dpt were both correlated with the season. Positive reactions to Df, Dpt, cockroach and weed were related to disease duration and positive reactions to cockroach were correlated with city residence. Multivariate analysis revealed that male positive reactions gradually decreased with age (≤ 60), in contrast to female (≤ 60) positive reactions. Statistical difference was observed between the genders with regard to AR incidence from 2016 to 2019. The positive rate of skin tests was highest in summer in men, whereas in women it was lowest in summer. The gender composition ratios of positive cases in Xiamen, Zhangzhou, and Quanzhou cities differed significantly. The proportion of patients with positive reactions to the allergens in the three cities decreased with age. The highest proportions of patients with positive reactions all occurred during summer in the three cities. Furthermore, there were statistically significant differences in the age composition ratios across the seasons. This study analyzed the distributional characteristics of AR allergens in Southern Fujian Province, China. These findings will inform specific immunotherapy for AR patients.

Cardiorespiratory fitness has declined among French children since 1999, although the decline appears to be getting smaller.

Cardiorespiratory fitness (CRF) is a strong marker of current and future health. The aim of this study was to assess the national temporal trends in CRF for French children and adolescents between 1999 and 2022. CRF data were obtained from several cross-sectional studies on 15 420 (51.1% boys) French children and adolescents aged 9-16 years between 1999 and 2022. The 20-m shuttle run test (20mSRT) estimated CRF. Body mass index (BMI) was calculated from measured height and body mass, with BMI z-scores (BMIz) calculated using WHO growth curves. The 20mSRT results were corrected for protocol and converted to z-scores (20mSRTz) using international sex- and age-specific norms. With additional adjustment for BMIz, temporal trends in mean 20mSRT performance (20mSRTz) were estimated using linear regression, with the distance max (Dmax) method used to locate a breakpoint and linear segments fitted to points below and above the breakpoint. Trends in distributional characteristics were assessed visually and described as the ratio of the coefficients of variation (CVs). After adjustment for protocol, age, sex, and BMIz, a large decline in mean 20mSRT performance (trend per decade (95% CI): -1.42 SDs (-1.45, -1.39) or -18.4% (95% CI: -18.8, -18.0)) between 1999 and 2022 was found. Dmax located a breakpoint for the two-linear-segment model in year 2010. There was a large decline in mean 20mSRT performance pre-2010 (trend per decade (95% CI): -2.31 SDs (-2.39, -2.24)), which reduced 0.06-fold to a negligible decline post-2010 (trend per decade (95% CI): -0.15 SD (-0.20, -0.10)). We also found that the trend in mean 20mSRT performance was not uniform across the population distribution. Between 1999 and 2022, there was a small trend in distributional asymmetry, with slightly smaller declines experienced by the high performers (above the 75th percentile). Our data suggest a large decline in the 20mSRT performance of French children and adolescents since 1999. This declining trend seems to have diminished considerably since 2010. Such declines in CRF could translate into declines in health status. Although a slowing in the declining trend in CRF in recent years is encouraging, more data are needed to confirm these findings.

The epidemiology and clinical features of post-exposure prophylaxis for rabies: A retrospective study of 9772 cases

BackgroundIn December 2015, the World Health Organization, the World Animal Health Organization, and the Food and Agriculture Organization of the United Nations convened the International Congress on the elimination of rabies in Geneva. How to use epidemiological factors of post-exposure prophylaxis to prevent rabies has become the focus of attention. ObjectiveTo analyze the epidemiological characteristics of 9772 patients with rabies in a four-year period in one hospital, to clarify the outbreak law of rabies and to explore the corresponding prevention and control strategies. MethodsThe epidemiological data of rabies patients were collected from the infectious disease reporting information management system of the hospital from July 2018 to June 2022. The distributional characteristics of 13 influencing factors were analyzed using the chi-square test and linear regression. ResultsThere was a significant correlation between the number of wounds and age, and the numbers of female and male patients were close. People over the age of 44 were more likely to get bites or scratches on their lower extremity (P<0.0001). There was a greater possibility for elderly people to be bitten by dogs (P<0.0001). Dogs preferred to bite or scratch lower limbs (P<0.0001), while cats upper limbs (P<0.0001). Upper limbs were more possibly attacked by animals at home (P<0.0001). There were significant correlations among exposure grade, wound treatment and number of wounds. Conclusions: Lower extremity protection is needed for the elderly and when encountering dogs, and more attention needs to be paid to the upper extremities when encountering cats and household pets, as well as pets that are cute but need to be protected from bites or scratches.

Deep Learning-Based Detection of Human Blastocyst Compartments with Fractal Dimension Estimation

In vitro fertilization (IVF) is an efficacious form of aided reproduction to deal with infertility. Human embryos are taken from the body, and these are kept in a supervised laboratory atmosphere during the IVF technique until they exhibit blastocyst properties. A human expert manually analyzes the morphometric properties of the blastocyst and its compartments to predict viability through manual microscopic evaluation. A few deep learning-based approaches deal with this task via semantic segmentation, but they are inaccurate and use expensive architecture. To automatically detect the human blastocyst compartments, we propose a parallel stream fusion network (PSF-Net) that performs the semantic segmentation of embryo microscopic images with inexpensive shallow architecture. The PSF-Net has a shallow architecture that combines the benefits of feature aggregation through depth-wise concatenation and element-wise summation, which helps the network to provide accurate detection using 0.7 million trainable parameters only. In addition, we compute fractal dimension estimation for all compartments of the blastocyst, providing medical experts with significant information regarding the distributional characteristics of blastocyst compartments. An open dataset of microscopic images of the human embryo is used to evaluate the proposed approach. The proposed method also demonstrates promising segmentation performance for all compartments of the blastocyst compared with state-of-the-art methods, achieving a mean Jaccard index (MJI) of 87.69%. The effectiveness of PSF-Net architecture is also confirmed with the ablation studies.

Review of marine alien isopods in Türkiye with two new records: of Paracerceis sculpta and Paranthura japonica

This study focuses on the alien isopod species recorded in the Turkish seas. To date, two alien species (Paradella dianae, Sphaeroma walkeri) have been reported in the country, accounting for almost 2.4% of the total number of isopod species occurring in the Turkish seas. The faunistic analysis of benthic samples collected from Dardanelles (Çanakkale) Straits and Izmir Bay revealed two new alien isopod species (Paranthura japonica and Paracerceis sculpta), on the Turkish coasts. Additionally, this is the first record of P. japonica in the Black Sea ecosystem. This study analyses and discusses the morphological, ecological and distributional characteristics of the species.

Influences of Snow Cover on the Thermal Regimes of Xing'an Permafrost in Northeast China in 1960s–2010s

ABSTRACTThe distributive characteristics of snow cover and their impacting mechanisms on ground thermal regimes in Northeast China remain evasive because of limited systematic studies. In this study, based on long‐term ground‐based observational data and auxiliary topographic data, geographically weighted regression kriging (GWRK) method and the temperature at the top of permafrost (TTOP) model were used to analyze the influences of snow cover duration (SCD) and average snow depth over the SCD (ASDSCD) on the thermal regimes of Xing'an permafrost in Northeast China in the 1960s–2010s. The results show a remarkable reduction of permafrost extent in Northeast China from the 1960s to 2010s, with an average reduction rate of 4.115 × 104 km2/decade. In permafrost regions, from the 1960s to 2010s, average SCD varied between 150 and 160 days, and the regional average of ASDSCD between 8 and 14 cm. Increases in ASDSCD led to a rise of TTOP. From the 1960s to 2010s, the regional average of ASDSCD increased by 3.53 cm, and that of TTOP by 2.02°C. The research results can provide scientific basis and data support for evaluating the responses of permafrost and cold region ecosystems to climate change in Northeast China.

Biased inferences about gender from names

How do alternative forms of reference to individuals—first, last, and full names—guide inferences about the gender of the referent? Given distributional correspondences between English first names and gender, first names provide probabilistic information about an individual's gender. While English last names do not vary with gender, men are more likely to be referred to by last name alone. Across four experiments, we demonstrate that inferences about gender are shaped by a persistent bias to infer that people are male, along with probabilistic information carried by the first name. When an individual was introduced by last name alone, participants overwhelmingly used he to subsequently refer to the person, suggesting that participants inferred that the person was male. This bias was still present when the individual was introduced using a first or full name, with participants less likely to use she than the distributional characteristics of the first names would predict. When explicitly asked to recall an individual’s gender who was introduced by last name alone, participants preferentially responded that the person was male. This bias persisted even when the person was introduced using a first or full name. Repeated reference attenuated, but did not eliminate, this bias. We discuss implications for models of how world knowledge is linked to language use.

Shanghai Crude Oil Futures Yield Volatility Study

Since the listing of Shanghai crude oil futures in March 2018 its market performance has gradually attracted attention. The closing price data of the futures for the period from March 26, 2018 to April 17, 2023 are selected as observations, and a GARCH model is fitted to the futures log return series to analyze the development of Shanghai crude oil futures and its future trend. The study finds that extreme events such as the New Crown epidemic and the Russia-Ukraine conflict had led to high volatility of Shanghai crude oil futures, but the volatility gradually stabilized in the context of the double cycle at home and abroad. The study compares the effectiveness of the GARCH(1,1)-T model and the GARCH(1,1)-GED model in fitting the logarithmic returns of the Shanghai crude oil futures market. the GARCH(1,1)-T model is able to better capture the asymmetry of the market volatility and the phenomenon of sharp peaks and thick tails after taking into account the distributional characteristics of the residuals. In the paper, the closing price of Shanghai crude oil futures in the next 10 days is predicted according to the GARCH(1,1)-T model, and the short-term prediction effect is better.

Comparison of Pressure-Based and Skin Friction-based Methods for the Determination of Flow Separation of a Circular Cylinder with Roundness Imperfection

Introduction: A delayed detached eddy simulation in OpenFOAM was performed to study flow separation of a circular cylinder with roundness imperfection up to 4% of its diameter at Reynolds numbers of 100, 3900, and 10! in normal flow. Method: The flow was considered to be Newtonian and incompressible. The separation position was determined independently based on surface pressure distribution and skin friction. Result:: Results show that the patterns of these distributions depend on both Reynolds number and roundness imperfection level, and flow separation in an imperfectly round cylinder may be induced by either an adverse pressure gradient or a Gentle Bend (GB) introduced by the roughness. For the separation point determined by the pressure-based method, its accuracy can be affected by the characteristic of pressure distribution near the separation point at low Reynolds numbers, and, thus, its physical validity needs to be verified by flow visualization at high Reynolds numbers. Conclusion: The skin friction-based method can accurately predict separation point for both perfectly and imperfectly round cylinders without additional information. When the roundness imperfection ratio reaches 2% and the Reynolds number reaches 3900, both approaches indicate that the flow separation point converges to the location of GB on the cylinder surface and the two sets of predicted separation points agree well.

Employing zero-inflated beta distribution in an exposure-response analysis of TYK2/JAK1 inhibitor brepocitinib in patients with plaque psoriasis.

Brepocitinib is an oral selective dual TYK2/JAK1 inhibitor and based on its cytokine inhibition profile is expected to provide therapeutic benefit in the treatment of plaque psoriasis. Efficacy data from a completed Phase 2a study in patients with moderate-to-severe plaque psoriasis were utilized to develop a population exposure-response model that can be employed to inform dose selection decisions for further clinical development. A modeling approach that employs the zero-inflated beta distribution was used to account for the bounded nature and distributional characteristics of the Psoriasis Area and Severity Index (PASI) score data. The developed exposure-response model provided an adequate description of the observed PASI scores across all the treatment arms tested and across both the induction and maintenance dosing periods of the study. In addition, the developed model exhibited a good predictive capacity with regard to the derived responder metrics (e.g., 75%/90%/100% improvement in PASI score [PASI75/90/100]). Clinical trial simulations indicated that the induction/maintenance dosing paradigm explored in this study does not offer any advantages from an efficacy perspective and that doses of 10, 30, and 60mg once-daily may be suitable candidates for clinical evaluation in subsequent Phase 2b studies.

Simulation models for aggregated data meta-analysis: Evaluation of pooling effect sizes and publication biases.

Simulation studies are commonly used to evaluate the performance of newly developed meta-analysis methods. For methodology that is developed for an aggregated data meta-analysis, researchers often resort to simulation of the aggregated data directly, instead of simulating individual participant data from which the aggregated data would be calculated in reality. Clearly, distributional characteristics of the aggregated data statistics may be derived from distributional assumptions of the underlying individual data, but they are often not made explicit in publications. This article provides the distribution of the aggregated data statistics that were derived from a heteroscedastic mixed effects model for continuous individual data and a procedure for directly simulating the aggregated data statistics. We also compare our simulation approach with other simulation approaches used in literature. We describe their theoretical differences and conduct a simulation study for three meta-analysis methods: DerSimonian and Laird method for pooling aggregated study effect sizes and the Trim & Fill and precision-effect test and precision-effect estimate with standard errors method for adjustment of publication bias. We demonstrate that the choice of simulation model for aggregated data may have an impact on (the conclusions of) the performance of the meta-analysis method. We recommend the use of multiple aggregated data simulation models to investigate the sensitivity in the performance of the meta-analysis method. Additionally, we recommend that researchers try to make the individual participant data model explicit and derive from this model the distributional consequences of the aggregated statistics to help select appropriate aggregated data simulation models.

Vibration characteristics of multilayer functionally graded microplates with variable thickness reinforced by graphene platelets resting on the viscoelastic medium under thermal effects

Due to their improved mechanical properties and adaptability, microplates with tailored variable thickness profiles are becoming essential parts of advanced micro- and nanoelectromechanical systems (MEMS and NEMS). This study conducts a thorough analytical analysis of the vibration properties of thermally loaded, multilayer functionally graded graphene platelet-reinforced composite (FG-GPLRC) microplates of linearly or parabolically varying thickness resting on viscoelastic medium under different boundary conditions. The Halpin–Tsai micromechanical model and the law of mixtures are employed to calculate the effective material characteristics for various reinforcement distributions in the microplate. These distributions encompass uniformly symmetric and asymmetric arrangements. The study utilized the first-order shear deformation theory (FSDT) in conjunction with the modified strain gradient theory (MSGT) and Hamilton's principle to generate the dynamic governing equations for the structure, accounting for size-dependent effects. The resulting equations are afterwards solved using the utilization of the Galerkin technique. This enables the evaluation of the proposed solution's correctness and precision. The impact of various factors on vibration behavior is investigated through numerical analysis. These factors encompass length scale parameters, temperature fluctuations, temperature distribution profiles, boundary conditions, the distribution pattern of the GPL, taper constants in both unidirectional and bidirectional scenarios, the weight fraction of the GPL.

The inconsistency of sentential subjects’ analysis in English

The article presents a critical analysis of approaches to defining sentential subjects as non-canonical syntactic units consisting of a finite or a non-finite clause. The major issue discussed is the position of the sentential subject in the tree structure debating whether it lands in subject position as a result of movement or it is base-generated in the subject position. Additionally, a claim is made that sentential subjects are not true subjects, but rather topics, suggesting that a different constituent occupies the canonical subject position. Therefore, sentential subjects appear to behave akin to both topics and regular subjects. They cannot generally occur in subject positions in embedded clauses, subject-auxiliary inversions and after topicalised units, yet they trigger subject-verb agreement. When considering these two alternatives, the author finds it important to distinguish between the pragmatic function and the syntactic position. Though subjects have been studied in various linguistic schools, a common consistent opinion on the sentential subject status has not been reached. The discrepancies in existing views are revealed in similar examples analyzed as grammatical or ungrammatical within different scientific frameworks. Distributional and transformational tests, along with the study of the information structure of the utterance aimed at proving the acceptability / unacceptability of sentential subjects show mixed results and sometimes contradictory analyses. The research suggests that ungrammatical sentential units are normally neutralised by alternative structures that function as syntactic blockers. To address the complexity in exploring sentential subject,the author advocates for a multifactor approach, which takes into account structural, distributional, weight ratio, semantic, pragmatic and psycholinguistic characteristics of subjects in a variety of configurations to ensure understanding the degree of subjecthood of syntactic structures and their systemic arrangement from the core to the periphery. In conclusion, the article emphasizes the need for a comprehensive approach to studying sentential subjects, acknowledging their multifaceted nature and aiming for a systemic understanding of subjecthood within linguistic frameworks.

Impact of initialization of a modified particle swarm optimization on cooperative source searching

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.