Temporal Characteristics Research Articles

Temporal and Spectral Characteristics of Persistent Emission and Special Bursts of Magnetar SGR J1935+2154 Based on Insight-HXMT

In October 2022, the magnetar SGR J1935+2154 entered the active outburst state. During the episode, the Insight-HXMT satellite carried out a long observation that lasted for 20 days. More than 300 bursts were detected, and a certain amount of persistent radiation signals were also accumulated. This paper mainly introduces the results of persistent radiation profile folding and period search based on Insight-HXMT data. At the same time, the burst phase distribution characteristics, spectral lag results of burst, the spectral characteristics of zero-lag bursts and the time-resolved spectral evolution characteristics of high-flux bursts are reported. We found that there is no significant delay feature during different energy bands for the bursts of SGR J1935+2154. The observed zero-lag burst does not have a unique spectrum. The time-resolved spectrum of the individual burst has consistent spectral types and spectral parameters at different time periods of the burst. We also find that the burst number phase distribution and the burst photon phase distribution have the same tendency to concentrate in specific regions of the persistent emission profile.

Optimization of mixed cargo allocation and stowage with heterogeneous vehicles

In order to solve the problem of low efficiency and high loading costs in business logistics and distribution operations, this paper classifies non-standard cargoes based on their spatial and temporal characteristics. Based on this, a dual-layer planning model for multi-vehicle cargoes loading and transportation is constructed to jointly optimize the loading process. This paper designs a space-inspired heuristic algorithm to solve the model and verifies the feasibility of the model. The results show that compared to a single vehicle type, the joint optimization of multi-vehicle combinations for loading and transportation has stronger adaptability and can better accommodate changes in cargo volume. Compared to the single vehicle type, not only did the loading rate increase by 18%, but also effectively reduced the transportation cost by 3%. Different proportions of spatial and temporal characteristic cargoes have different effects on the loading and transportation. Adjusting the proportion of cargoes that can be placed on both upper and lower layers to 50% can better utilize transportation resources such as vehicles. Increasing the proportion of urgent cargoes can improve the economic efficiency of loading and transportation.

Rapid dynamics of electrophysiological connectome states are heritable

Abstract Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infra-slow (<0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N = 928, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60–500 ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of dynamic spatial features, specifically states’ Modularity and connectivity pattern. We conclude that genetic effects shape individuals’ connectome dynamics at rapid timescales, specifically states’ overall occurrence and sequencing.

Multi-scale population analysis unit construction method considering scene feature variability and long/short-term patterns in spatiotemporal population activities

ABSTRACT Differences in the spatial scale and component elements of urban scenes affect the analysis of spatiotemporal changes in population distribution (PD) patterns. Fixed scales and geometric forms constrained previous analyses of PD and spatiotemporal changes, which neglected the realistic characteristics of urban scenarios. These limitations hinder the ability to capture the diversity and spatiotemporal representation of PD patterns across multiple spatial scales. This study developed a multi-spatial scale population analysis unit (PAU) construction method considering the scenario heterogeneity and long/short-term patterns of spatiotemporal population changes (PC). Initially, we decompose the multi-scale changes in population time series patterns and their relationships with scene features along the temporal dimension to analyze the primary and secondary factors. Starting from the temporal characteristics of the PD and PC, we established a validation and correction method for the factors. Finally, combined with a multi-feature clustering method, a multi-scale PAU construction method driven by scene feature factors is proposed. Experiments were conducted at different spatial scales in both routine and emergent scenarios. The results indicated that this method can help to obtain more homogeneous analysis regions and enhance the stability and phase pattern representations of changes in PDs, thereby improving the interpretability of analytical results.

Handwritten Based Alzheimer Disease Prediction from One Dimensional Datasets Using Deep Learning

Because to their high cost, sensitivity, and difficulty in completing surgeries, brain-related disorders are among the most challenging conditions. On the other hand, since the procedure's outcomes could be negative, the operation itself does not have to succeed. Alzheimer's disease, which affects adults and causes varied degrees of memory loss and knowledge forgetfulness, is one of the most prevalent brain diseases. based on the state of each patient. For these reasons, using user-handwritten datasets to categorise memory loss and determine the patient's evaluation of Alzheimer's disease at every given level is crucial. This work offers a novel method for predicting Alzheimer's disease by using advanced deep learning techniques on handwritten data. Alzheimer's is a degenerative. Alzheimer's disease, a neurological condition that progresses and necessitates prompt diagnosis and appropriate treatment. Traditional diagnostic techniques are mostly based on clinical evaluations and imaging, which are frequently inaccessible and expensive. This study investigates the unrealized potential of handwritten data as a special kind of Alzheimer's disease prediction. The dataset provides a broad picture of cognitive impairments by containing handwritten samples collected from individuals in varying states of cognition. Using deep learning architectures, such as the multi-layer perceptron method, the suggested model takes use of the temporal dependencies found in sequential handwritten patterns. These designs show promise for ensuring the temporal characteristics of handwritten data are captured with subtle features. The handwritten input is converted into a format suitable for deep learning using feature extraction techniques, which helps with efficient model training. A thorough assessment of the model's performance is conducted using common metrics including specificity, sensitivity, and accuracy. The goal is to determine whether the model can correctly forecast Alzheimer's disease based just on the unique features present in handwritten samples.

The Temporal and Spatial Evolution Characteristics and Driving Factors of Ecosystem Service Bundles in Anhui Province, China

Identifying ecosystem service bundles and their long-term evolutionary characteristics is essential for the overall enhancement of regional ecosystem services, as well as the division and management of functional areas, providing a basis for decision-making in formulating ecological and environmental protection policies, as well as regional development planning. Based on land use, remote sensing, and meteorological data obtained from Anhui Province, this study assessed six important ecosystem service functions, including food production (FP), water yield (WY), carbon sequestration (CS), soil conservation (SC), habitat quality (HQ), and landscape aesthetics (LA), at the township scale in 2000, 2010, and 2020. On this basis, the k-means clustering method was used to identify ecosystem service bundles, analyze the spatio-temporal evolution trajectory of service bundles, and explore the driving factors of the spatio-temporal evolution of ecosystem service bundles using GeoDetector 2015 The results indicate the following: (1) At the spatial level, diverse ecosystem services demonstrate pronounced spatial differentiation. The distribution pattern of HQ, carbon fixation, and SC services is generally lower in the north and higher in the south, with areas of high value predominantly located in the western Dabie Mountains and the mountains of Southern Anhui. Conversely, FP services exhibit the reverse pattern, and WY services display a gradual increase from north to south, while cultural services are more dispersed, with areas of high value primarily located in the western Dabie Mountains, the Yangtze River Basin, and other locations. On the temporal scale, WY, SC, and FP services mainly exhibit an increasing trend, marked by a significant increase, whereas other services tend to present a decreasing trend. (2) Anhui Province can be categorized into four distinct types of service bundles: the grain production bundle (GPB), mountain ecological conservation bundle (MECB), urban living bundle (ULB), and core protection bundle (CPB). Ecosystem service bundles exhibit clear spatial differentiation, and identical service bundles demonstrate substantial clustering in space. Between 2000 and 2020, ecosystem service bundles displayed a marked spatio-temporal evolution, with the prevalence of GPBs diminishing, whereas the share of ULBs progressively increased, and the number of MECBs and CPBs remained largely stable. (3) In the spatio-temporal evolution process, the average annual precipitation, the proportion of forest land, and slope constitute the principal natural factors influencing the spatio-temporal evolution of ecosystem service bundles, while the proportion of construction land represents the primary socio-economic factor, with natural factors exerting a more significant influence than socio-economic factors.

Urban landscape sustainability in karst mountainous cities: A landscape resilience perspective

In the context of the rapid progress of global urbanization, the massive encroachment of social landscapes into ecological and productive landscapes has led to a series of environmental problems. Furthermore, analyzing the landscape resilience could effectively reveal the sustainable development ability of the urban landscape. This study establishes a social-ecological productive landscape resilience (SEPLR) evaluation system and reveals trade-offs and synergies between different landscape types and resilience. Finally, this study provides landscape management zonings based on the spatial and temporal dynamic characteristics of landscape resilience and subsystem resilience. The findings showed that: (1) The CUAG has significant landscape heterogeneity and change drastically, which is mainly manifested by the massive encroachment of social landscape into productive landscape. (2) The SEPLR of CUAG decreased slightly by 0.75 % over the decade, with significant changes of spatial distribution. (3) The comprehensive remediation areas and social development areas are the dominant management zones. The findings could be incorporated into the decision-making of land use trade-off development in CUAG to promote the coordinated development of social-ecological productive systems and improve the sustainability of urban landscape.

Research on the Temporal and Spatial Evolution Characteristics of the Ecological Level of Urban Production Space: A Case Study of the Upper Reaches of the Yangtze River, China

Research on the Temporal and Spatial Evolution Characteristics of the Ecological Level of Urban Production Space: A Case Study of the Upper Reaches of the Yangtze River, China

Bayesian inference-based estimation of hourly primary and secondary organic carbon in suburban Hong Kong: multi-temporal-scale variations and evolution characteristics during PM2.5 episodes

Abstract. Observation-based data of primary and secondary organic carbon in ambient particulate matter (PM) are essential for model evaluation, climate and air quality research, health effect assessments, and mitigation policy development. Since there are no direct measurement tools available to quantify primary organic (POC) and secondary organic carbon (SOC) as separate quantities, their estimation relies on inference approaches using relevant measurable PM constituents. In this study, we measured hourly carbonaceous components and major ions in PM2.5 for a year and a half in suburban Hong Kong from July 2020 to December 2021. We differentiated POC and SOC using a novel Bayesian inference approach. The hourly POC and SOC data allowed us to examine temporal characteristics varying from diurnal and weekly patterns to seasonal variations, as well as their evolution characteristics during individual PM2.5 episodes. A total of 65 city-wide PM2.5 episodes were identified throughout the entire study period, with SOC contributions during individual episodes varying from 10 % to 66 %. In summertime typhoon episodes, elevated SOC levels were observed during daytime hours, and high temperature and NOx levels were identified as significant factors contributing to episodic SOC formation. Winter haze episodes exhibited high SOC levels, likely due to persistent influences from regional transport originating from the northern region to the sampling site. Enhanced SOC formation was observed with increase in the nocturnal NO3 radical (indicated by the surrogate quantity of [NO2][O3]) and under conditions characterized by high water content and strong acidity. These results suggest that both NO3 chemistry and acid-catalyzed aqueous-phase reactions likely make notable contributions to SOC formation during winter haze episodes. The methodology employed in this study for estimating POC and SOC provides practical guidance for other locations with similar monitoring capabilities in place. The availability of hourly POC and SOC data is invaluable for evaluating and improving atmospheric models, as well as understanding the evolution processes of PM pollution episodes. This, in turn, leads to more accurate model predictions and a better understanding of the contributing sources and processes.

Application of multiplatform remote sensing data over East Asia Ocean: aerosol characteristics and aerosol types.

It is important to explore the characteristics and rules of atmospheric aerosol in the East Asian Sea for monitoring and evaluating atmospheric environmental quality. Based on Aerosol Robot Network (AERONET), Visible Infrared Imaging Radiometer (VIIRS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the temporal and spatial variation characteristics and differences of aerosol parameters and types in the East Asian Sea were studied by using figure classification method (FIGCM), aerosol optical depth (AOD)440-Angstrom exponent (AE)440-870 method (AA1M), and AOD550-AE490-670 method (AA2M). The results show that the seasonal variation trend of aerosol characteristics and types is obvious in East Asia Sea. AOD, volume concentration (Cv), and aerosol effective radius (reff) in the Bohai-Yellow Sea and the Sea of Japan in autumn are lower than those in other seasons, and the occurrence frequency of ocean-type aerosols is high. Different from the Bohai-Yellow Sea and Sea of Japan, human activities in winter, summer, and autumn seriously affect the air quality in the East China Sea and South China Sea. Especially at the Taipei CWB site, from aerosol parameters and high biomass burning/urban industrial (BB/UI) aerosol, human activity is an important factor for high pollution at the Taipei CWB site. Aerosol types of AA1M, FIGCM, AA2M, and CALIPSO were compared at Anmyon and Yonsei University sites in the Bohai-Yellow Sea in March 2020. The results show that aerosol types based on threshold classification methods generally have higher mixed aerosol results, and the marine (MA) results of AA1M, FIGCM, and AA2M are close to the clean marine aerosol results of CALIPSO. Comparing the results of AA 2M and CALIPSO on a spatial scale, it is found that the clean marine aerosol proportion identified by CALIPSO (0.38, 0.48, 0.82) is consistent with the MA proportion identified by AA 2M (0.43, 0.46, 0.97) in the East China Sea, South China Sea, and Western Pacific Ocean.

Refinement of Different Frequency Bands of Geomagnetic Vertical Intensity Polarization Anomalies before M > 5.5 Earthquakes.

Geomagnetic vertical intensity polarization is a method with a clear mechanism, mature processing methods, and a strong ability to extract anomalous information in the quantitative analysis of seismogenic geomagnetic disturbances. The existing analyses of geomagnetic vertical intensity polarization are all based on the 5~100 s frequency band without refinement of the partitioning process. Although many successful results have been obtained, there are still two problems in the process of extracting anomalies: the geomagnetic anomalies that satisfy the determination criteria are still high in occurrence frequency; and the anomalies are distributed over too large an area in space, which leads to difficulties in determining the location of the epicenter. In this study, based on observations from western China, where fluxgate observation points are positioned in areas with frequent, densely distributed medium-strength earthquakes, we refined the frequency bands of geomagnetic vertical intensity polarization, recalculated the spatial and temporal evolution characteristics of geomagnetic disturbances before earthquakes, and improved the crossover frequency anomaly prediction index while promoting the application of the method in earthquake forecasting.

Does environmental decentralization improve industrial ecology? Evidence from China's Yangtze River Economic Belt

The rational division of environmental management powers between governments is the institutional basis for promoting the transformation of industrial ecology (IE). Based on panel data of 105 prefecture-level cities in the Yangtze River Economic Belt (YREB) of China from 2006 to 2020, this study analyzes the spatial and temporal evolution characteristics of IE and environmental decentralization (ED) in the YREB by constructing evaluation index systems. The spatial econometric model is used to explore the effects of ED on IE under economic growth pressure (EGP) and officials’ tenure (LT). The results indicate that regional differences in IE in the YREB exhibit a fluctuating downward trend and that the imbalance between regions is the leading cause of overall differences. Furthermore, IE presents a significant spatial positive correlation. ED has a restraining effect on IE and is regulated by EGP and LT. Moderate EGP can mitigate the negative effects of ED on IE, while LT intensifies the negative effects of ED on IE. Heterogeneity exists in the relationship between ED and IE in regional locations and pollution control pressure areas. These results are significant for developing a modern industrial system, formulating differentiated environmental protection strategies, and improving the government performance appraisal system.

Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Public bicycle systems (PBSs) serve as the ‘last mile’ of public transportation for urban residents, yet the problem of the difficulty in borrowing and returning bicycles during peak hours remains a major bottleneck restricting the intelligent and efficient operation of public bicycles. Previous studies have proposed reasonable models and efficient algorithms for optimizing public bicycle scheduling, but there is still a lack of consideration for actual road network distances between stations and the temporal characteristics of demand at rental points in the model construction process. Therefore, this paper aims to construct a public bicycle dispatch framework based on the spatiotemporal characteristics of borrowing and returning demands. Firstly, the spatiotemporal distribution characteristics of borrowing and returning demands for public bicycles are explored, the origin–destination (OD) correlation coefficients are defined, and the intensity of connections between rental point areas is analyzed. Secondly, based on the temporal characteristics of rental point demands, a random forest prediction model is constructed with weather factors, time characteristics, and rental point locations as feature variables, and station bicycle-borrowing and -returning demands as the target variable. Finally, bicycle dispatch regions are delineated based on actual path distances between stations and OD correlation coefficients, and a public bicycle regional dispatch optimization method is established. Taking the PBS in Ningbo City as an example, the balancing optimization framework proposed in this paper is validated. The results show that the regional dispatch optimization method proposed in this paper can achieve optimized dispatch of public bicycles during peak hours. Additionally, compared with the Taboo search algorithm (TSA), the genetic algorithm (GA) exhibits a 11.1% reduction in rebalancing time and a 40.4% reduction in trip cost.

Geometry-induced process and part characteristics in support-free powder bed fusion of polypropylene at room temperature

AbstractLaser-based powder bed fusion (LPBF) of semi-crystalline polymers enables the support-free layer-wise manufacturing of geometrically diverse, complex components. In contrast to the established quasi-isothermal powder bed fusion of polymers at elevated temperatures, non-isothermal, cold processing strategies allow to significantly extend the range of applicable material systems. Relying on the superposition of discretized, fractal exposure strategies and the implicit mesoscopic compensation of crystallization shrinkage, the support-free LPBF of polypropylene at room temperature is demonstrated. The present paper displays the temporally and spatially discrete exposure of superposed fractal, space-filling curves that enable the support-free LPBF of polypropylene through combining the mesoscopic compensation of crystallization shrinkage and the laser-induced minimization of thermal shrinkage through the implementation of pre-exposure scans. The non-isothermal processing regime was observed to exhibit an intrinsic robustness towards the influence of processing parameters on emerging peak temperatures while showing a significant extent of accumulated heat within manufactured parts. Complementary mechanical characterizations showed an orientation-dependent influence of the applied energy density on emerging mechanical properties, correlated with geometry-dependent temporal process characteristics that implicitly influence the available coalescence time and the timespan available for the thermal homogenization.

Global Flash Droughts Characteristics: Onset, Duration, and Extent at Watershed Scales

AbstractAddressing impacts of flash droughts (FDs) on the water‐food nexus requires a understanding of FD mechanisms and drivers at the watershed level. Examining climatic drivers, dry and wet spell lengths from 1980 to 2019, we analyzed FD spatial and temporal characteristics, emphasizing areal extent, onset time, and duration. Our findings reveal substantial variations in FDs among different watersheds. Notably, watersheds in the Southern Hemisphere are witnessing expanding, faster‐developing, and longer‐lasting FDs, aligning with climate variations in precipitation and temperature. Additionally, at the watershed scale, the onset and duration of FDs are more influenced by the intensity (magnitude and variability) of climatic drivers than the average length of wet and dry periods. FD‐extents, however, correlate with both climatic conditions and wet and dry periods, underscoring watershed connectivity. Ultimately, our results underscore the necessity for research to comprehend the interplay between FDs and watershed characteristics and how it manifests in overall water resource management.

Analysis of the temporal and spatial characteristics of lunar reconnaissance orbiter’s orbit error based on multi-coverage narrow angle camera images

ABSTRACT Photogrammetric mapping of large area of the lunar surface using high-resolution orbiter images is very challenging due to increasing spatial resolution and lack of a comprehensive understanding of orbit error characteristics. The Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images are currently one of the highest-resolution images of the Moon available. The study of LRO orbit errors from the perspective of mapping applications is important for high accuracy mapping of large areas using NAC images. This paper proposes a method for orbit error estimation using multi-coverage NAC images. First, available NAC images covering the study regions are orthorectified using ISIS. Then, the impact crater feature matching algorithm is used to match the multi-coverage images. Finally, estimation of the orbit error is achieved through statistical analysis of a large number of homologous points obtained from the matching. The orbit error analyses of nine representative regions on the Moon show that in most cases the refined ephemeris provided by the LOLA/GRAIL team can significantly reduce the orbit error. The orbit errors show a sawtooth variation with a period of about one year, which have been stable for a long time and tend to increase slightly in recent years. There is a slight variation of the orbit error in space, which is even less obvious when using the refined ephemeris. The general tendency is for the far side to be larger than the near side at the same latitude, and for the higher latitudes to be larger than the lower latitudes. The proposed method and analysis results provide a reference for selection and processing of multi-coverage LROC NAC images for high-resolution large-area mapping.

Characterization and Multi-Scenario Prediction of Habitat Quality Evolution in the Bosten Lake Watershed Based on the InVEST and PLUS Models

Habitat quality is an important basis for human well-being and the achievement of sustainable development. Based on land-use data for the Bosten Lake Basin in 2000, 2005, 2010, 2015, and 2022, the PLUS and InVEST models are applied in this study to predict and analyze land-use changes and explore the spatial and temporal evolution characteristics of the region’s habitat quality. Additionally, we use a geographic detector model to reveal the drivers of spatial variation in habitat quality. The results show that: (1) Land use in Bosten Lake Basin is dominated by grassland and bare land, with an area share of 93.21%. Habitat quality shows a trend of degradation followed by improvement, with a spatial pattern of high in the northwest and low in the southeast. (2) Habitat quality in 2030 increased from 2022 in all cases, with a mean of 0.354 for the natural development scenario, a maximum of 0.355 for the ecological development scenario, and a minimum of 0.353 for the economic development scenario. (3) The main drivers affecting habitat quality in the Bosten Lake watershed are DEM, mean annual precipitation (MAP), and GDP per capita. X1∩X4 (0.50) and X4∩X10 (0.51) are the interaction factors with the largest dominant effect in 2000, 2010 and 2020, respectively.

Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Cluster based Wireless Sensor Network

Wireless Sensor Network highly exposed to certain security attacks due to various uncertainties of the network which lead to node compromise. Hence considerable attention has been made by researcher during scheduling of the mobile sink to data collection of sensed information from the sensor nodes. Many secure data aggregation mechanism has been proposed to withstand the network against these kind of attack. Despite of many advantageous, those architectures will lead to several limitations such as large propagation delay and high energy consumption and spatial and temporal uncertainties. In order to mitigate those challenges, Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Wireless Sensor Network is designed. It should be capable achieving energy efficiency and collision avoidance due to uncertainty in transmission and reception. Proposed model eliminates the packet collision and poor channel utilization challenges. Initially, node is partitioned and cluster head is selected based on the highest energy density of the node in the particular location. Employing Full duplex channel easily examines the spatial and temporal characteristic of the node through utilization cache information of routing table. It further helps to build the light weight scheme with behavioral strategies of the nodes for secure propagation of the sensed data to the mobile sink. Light weight scheme deployed in mobile sinks estimate the node trustworthiness and simultaneously collects the secure aggregated data packets of the sensor node through cluster head against dynamic time slots concurrently .Comparing with traditional secure routing architecture, simulation results demonstrates that proposed architecture can reduce the propagation delay against various uncertainties and maximizes the life time of the network on increasing the networking performances with respect to packet delivery ratio, throughput and propagation delay. Finally proposed model obtains the cooperative communication of sensor node information to the base station through mobile sink on heterogeneous wireless sensor network environment. DOI: https://doi.org/10.52783/pst.387

Data-Driven ICS Network Simulation for Synthetic Data Generation

Data-Driven ICS Network Simulation for Synthetic Data Generation

Model Touch Pointing and Detect Parkinson's Disease via a Mobile Game

Touch pointing is one of the primary interaction actions on mobile devices. In this research, we aim to (1) model touch pointing for people with Parkinson's Disease (PD), and (2) detect PD via touch pointing. We created a mobile game called MoleBuster in which a user performs a sequence of pointing actions. Our study with 40 participants shows that PD participants exhibited distinct pointing behavior. PD participants were much slower and had greater variances in movement time (MT), while their error rate was slightly lower than age-matched non-PD participants, indicating PD participants traded speed for accuracy. The nominal width Finger-Fitts law showed greater fitness than Fitts' law, suggesting this model should be adopted in lieu of Fitts' law to guide mobile interface design for PD users. We also proposed a CNN-Transformer-based neural network model to detect PD. Taking touch pointing data and comfort rating of finger movement as input, this model achieved an AUC of 0.97 and sensitivity of 0.95 in leave-one-user-out cross-validation. Overall, our research contributes models that reveal the temporal and spatial characteristics of touch pointing for PD users, and provide a new method (CNN-Transformer model) and a mobile game (MoleBuster) for convenient PD detection.