Search Results (27)

Previous research on financial contagion has mostly looked at volatility spillovers using static or fixed parameter models. These models don’t always take into account how inter-market links change and depend on frequency during big crises. This study fills in that gap by looking at how changes in volatility in the G20 equity markets affected four big global events: the global financial crisis of 2008, the European debt crisis, the COVID-19 pandemic, and the Russia-Ukraine war. The study uses a Time-Varying Parameter Vector Autoregression (TVP VAR) framework along with the Baruník-Křehlík frequency domain spillover measure to look at how connectedness changes over short-term (1–5 days) and long-term (5–Inf days) time periods. The results show that systemic connectedness changes a lot during crises. For example, the Total Connectedness Index (TCI) was 24–25 percent during the GFC and EDC, 34 percent during COVID-19, and a huge jump to 60 percent during the Russia-Ukraine war. During the global financial crisis and the war between Russia and Ukraine, the US constantly emerged as the largest transmitter. During the European debt crisis, on the other hand, emerging markets like Turkey, South Africa, and Japan acted as net transmitters. During all crisis times, short-term spillovers are the most common. This shows how important high-frequency volatility transmission is. This study is different from others because it uses both time-varying and frequency domain views. This gives us a better idea of how crises change the way global finances are linked. The results are very important for policymakers and investors because they show how important it is to coordinate risk management, improve market safety, and make systemic stress testing better in a global financial world. Full article

Objectives: Sprinting and jumping abilities are key determinants of basketball performance. This study aims to analyze changes in sprinting and jumping performance among professional basketball players during the preseason and to determine whether these adaptations are influenced by specific playing positions (Guards vs. Bigs). Methods: A total of 106 professional basketball players from European leagues were evaluated twice over a 6-week preseason. Neuromuscular assessments included linear sprints (5, 10, and 20 m), a change of direction test, curved sprints, and multiple jump tests: Squat Jump (SJ), Countermovement Jump (CMJ), Single-Leg CMJ (SL-CMJ) and Arm-Swing CMJ (CMJA), Single Leg Hop for Distance (SHDJ), Lateral Bound Jump (LBJ), and Single-Leg Repeated Jumps (SLRJ). The training program integrated 6–8 weekly basketball-specific technical–tactical sessions with two to three strength and conditioning sessions targeting maximal strength, power, and hypertrophy. Results: Players significantly improved linear and curved sprint performance, and jumping ability, particularly CMJ, CMJA, and right-leg SHDJ. Minimal changes were observed in SJ, LBJ, and SLRJ. Positional differences were small, with Guards showing greater gains in CMJA than Bigs (6.85% vs. 1.87%). Conclusions: A 6-week preseason training program may be associated with improvements in sprinting (linear 5, 10, 20 m, and curved sprint) and vertical jump performance (CMJ, CMJA, SHDJ) in professional basketball players, with limited influence of playing position. Guards appear to benefit more from arm-swing vertical jump development. Full article

The digital creative industries have emerged as a critical driver of regional economic transformation, upgrading, and sustainable development. While previous research has primarily focused on creative industry layout and agglomeration in urban areas, with the integration of digital technology and the creative industry, existing research has an insufficient explanation for the digital creative industry. Specifically, few people have studied the spatial distribution and diffusion of digital creative industries in emerging economies from the macro-regional level. To address this gap, this study analyzes the spatial diffusion mode and regional spatial response law of digital creative industries in the Yangtze River Delta during three critical time windows (2016, 2019, and 2022) in the context of national strategy implementation. A range of spatial analysis technologies is utilized to process the full sample of big data from digital creative industries. This study utilizes OLS and a quantile regression model to determine the dominant factors that affect spatial diffusion and response in the digital creative industries. The results demonstrate that, against the backdrop of regional development strategies, digital creative industries exhibit a variety of diffusion modes, including contagious, hierarchical, corridor, and jump diffusion. The response of industries to regional strategies has different rules in terms of regional space, urban development, and sub-industries. Furthermore, the comprehensive influence of institutional environment, urban economy, development and innovation significantly impacts industrial spatial diffusion and regional response. Among them, government investment in science and technology and the number of universities have consistently been important influencing factors, and policy exhibits nonlinear effects and asymmetric characteristics on industry agglomeration and diffusion. This study enhances the understanding of digital creative industry development in the YRD and offers a theoretical basis for optimizing regional industrial spatial structure and promoting the sustainable development of digital industries. Full article

In Korea, the Public Health Center Mobile Healthcare Project was implemented in 2016. This project utilizes Information and Communication Technology (ICT) and big data to establish a health-related service foundation and a healthcare service operation system. Equipment and methods: This study recruited 1261 adolescents (660 males (13.40 ± 1.14 years, 156.12 ± 10.59 cm) and 601 females (13.51 ± 1.23 years, 154.45 ± 7.48 cm)) from 22 public health centers nationwide. Smart bands were provided, and the ‘Future Health’ application (APP) was installed on personal smartphones to assess body composition, physical fitness, and physical activity. Results: A paired sample t-test revealed height, 20 m shuttle run, grip strength, and long jump scores significantly differed after 24 weeks in males. Females exhibited significant height, 20 m shuttle run, grip strength, sit-ups, and long jump differences. Moderate physical activity (MPA, p < 0.001), vigorous physical activity (VPA, p < 0.001), and moderate-to-vigorous physical activity (MVPA, p < 0.001) were significantly different after 24 weeks in adolescents. These results establish that an ICT-based health promotion service can provide adolescent students with individual information from a centralized organization to monitor health behaviors and receive feedback regardless of location in South Korea. Full article

What if you could identify someone’s right palmprint just by looking at their left—and vice versa? That is exactly what I set out to do. I built a specially adapted Siamese network that only needs one palm to reliably recognize the other, making biometric systems far more flexible in everyday settings. My solution rests on two simple but powerful ideas. First, Anchor Embedding through Feature Aggregation (AnchorEFA) creates a “super-anchor” by averaging four palmprint samples from the same person. This pooled anchor smooths out noise and highlights the consistent patterns shared between left and right palms. Second, I use a Concatenated Similarity Measurement—combining Euclidean distance with Element-wise Absolute Difference (EAD)—so the model can pick up both big structural similarities and tiny textural differences. I tested this approach on three public datasets (POLYU_Left_Right, TongjiS1_Left_Right, and CASIA_Left_Right) and saw a clear jump in accuracy compared to traditional methods. In fact, my four-sample AnchorEFA plus hybrid similarity metric did not just beat the baseline—it set a new benchmark for cross-palmprint recognition. In short, recognizing a palmprint from its opposite pair is not just feasible—it is practical, accurate, and ready for real-world use. This work opens the door to more secure, user-friendly biometric systems that still work even when only one palmprint is available. Full article

Basketball neuromuscular demands are highly position-dependent, making it important to consider this factor in performance assessment. This study aimed to analyze the validity and reliability of jumping and linear sprinting tests for professional basketball players based on their playing position. A total of 102 professional basketball players, classified as Bigs and Guards, were assessed during the preseason through Squat Jump (SJ), Countermovement Jump (CMJ), Single-Leg CMJ (SL-CMJ), Arm Swing CMJ (CMJA), and linear sprinting over 5, 10, and 20 m. Relative reliability analysis was carried out by calculating the Intraclass Correlation Index (ICC), and the coefficient of variation (CV) was used as an absolute reliability indicator. The jumping and linear sprinting tests showed good to excellent relative reliability (ICC: 0.81–0.97) and absolute reliability (CV: 0.1–2.6) with a minimum detectable change ranging from 5.38 to 20.82% and from 4.76 to 10.43% for jumping and linear sprinting tests, respectively. Both Bigs and Guards showed excellent absolute reliability in all tests. Bigs showed greater ICC than Guards in SJ, CMJ, CMJA, and the 10 and 20 m sprints, while Guards outperformed in the 5 m sprint. SL-CMJ showed greater absolute reliability for Bigs, while relative reliability was higher for Guards. In conclusion, these findings may aid basketball physical coaches in the selection of the most suitable jumping and sprinting tests for preseason neuromuscular performance monitoring based on players’ playing position. Full article

This study focuses on the coupling and coordination between China’s new-type urbanization (NU) and transportation carbon emission efficiency (CET), revealing its spatial and temporal evolution patterns and driving factors. In recent years, the rapid rise of the digital economy has profoundly reshaped traditional industrial structures. It has catalyzed new forms of production and consumption and opened up new pathways for carbon reduction. This makes synergies between NU and CET increasingly important for realizing a low-carbon transition. In addition, digital infrastructures such as 5G networks and big data platforms promote energy efficiency and facilitate industrial upgrading. It also promotes the integration of low-carbon goals into urban governance, thus strengthening the linkages between NU and CET. The study aims to provide a scientific basis for regional synergistic development and green transformation for the goal of “dual carbon”. Based on the panel data of 30 provinces in China from 2004 to 2021, the study adopts the entropy weight method and the super-efficiency SBM model to quantify NU and CET, and then analyzes their spatial and temporal interactions and spatial spillovers by combining the coupled coordination degree model and the spatial Durbin model. The following is found: (1) NU and CET show a spatial pattern of “leading in the east and lagging in the west”, and are optimized over time, but with significant regional differences; (2) the degree of coupling coordination jumps from “basic disorder” to “basic coordination”, but has not yet reached the level of advanced coordination, with significant spatial clustering characteristics (Moran’s I index between 0.244 and 0.461); (3) labor force structure, transportation and energy intensity, industrial structure and scientific and technological innovation are the core factors driving the coupled coordination, and have significant spatial spillover effects, while government intervention and per capita income have limited roles. This paper innovatively reveals the two-way synergistic mechanism of NU and CET, breaks through the traditional unidirectional research framework, and systematically analyzes the two-way feedback effect of the two. A multidimensional NU evaluation system is constructed to overcome the limitations of the previous single economic or demographic dimension, and comprehensively portray the comprehensive effect of new urbanization. A multi-dimensional coupled coordination measurement framework is proposed to quantify the synergistic evolution law of NU and CET from the perspective of spatio-temporal dynamics and spatial correlation. The spatial spillover paths of key factors are finally quantified. The findings provide decision-making references for optimizing low-carbon policies, promoting green transformation of transportation, and taking advantage of the digital economy. Full article

This study investigates the spatiotemporal density aggregation and pattern distribution of vessel traffic amidst bustling maritime logistics scenarios. Firstly, a relatively new spatiotemporal segmentation and reconstruction method is proposed for ship AIS trajectories to address trajectory disruptions caused by berthing, anchorage, and other factors. Subsequently, a trajectory filtering algorithm utilizing time window panning is introduced to mitigate position jumps and deviation errors in trajectory points, ensuring that the dynamic trajectory adheres to the spatiotemporal correlations of ship motion. Secondly, to establish a geographical spatial mapping of dynamic trajectories, spatial gridding is applied to maritime traffic areas. By associating the geographical space of traffic activities with the temporal attributes of dynamic trajectories, a dynamic trajectory temporal density model is constructed. Finally, a case study is conducted to evaluate the effectiveness and applicability of the proposed method in identifying spatiotemporal patterns of maritime traffic and spatiotemporal density aggregation states. The results show that the proposed method can identify dynamic trajectory traffic patterns after the application of compression algorithms, providing a novel approach to studying the spatiotemporal aggregation of maritime traffic in the era of big data. Full article

Objectives: The purpose of the present study was to evaluate the level of physical capacities of Italian American Football (AF) players and compare their performances with published data of American college players. A secondary aim was to assess whether the performance of Italian players in the NFL Combine tests has improved over time compared to previously tested players of similar competitive level. A total of 41 Italian AF players (age 28.1 ± 4.7 y, stature 181.1 ± 5.9 cm, body mass 98.3 ± 17.8 kg) competing in the 2020/2021 Division I Championship, participated in this study and performed the NFL Combine test battery. Methods: The NFL Combine test battery includes the 40-yard dash, the 20-yard shuttle, the 3-cone drill tests, the broad jump test, the vertical jump test, and the maximum number of repetitions at bench press with a 100 kg load. Players were divided into three groups based on their playing position: skill players (SP = 14), big skill players (BSP = 9), or linemen (LM = 13). In addition, players’ performance scores were normalized to their stature and body weight. Results: Italian players showed lower performances in all the six tests compared to American college players. Significant differences were observed between player positions. Normalized performances were significantly lower in Italian compared to American players. Conclusions: Despite an improving trend in the NFL Combine tests being registered in Italian AF players, a relevant gap still exists compared to their US counterparts. Full article

Background/Objectives: Using advanced methodologies may enhance athlete profiling. This study profiled morphological and laboratory-derived performance biomechanics by position of American-style football players training for the draft. Methods: Fifty-five players were categorized into three groups: Big (e.g., lineman; n = 17), Big–skill (e.g., tight end; n = 11), and Skill (e.g., receiver; n = 27). Body fat (BF%), lean body mass (LBM), and total body mass were measured using a bioelectrical impedance device. Running ground reaction force (GRF) and ground contact time (GCT) were obtained using an instrumented treadmill synchronized with a motion capture system. Dual uniaxial force plates captured countermovement jump height (CMJ-JH), normalized peak power (CMJ-NPP), and reactive strength. Asymmetry was calculated for running force, GCT, and CMJ eccentric and concentric impulse (IMP). MANOVA determined between-group differences, and radar plots for morphological and performance characteristics were created using Z-scores. Results: There was a between-group difference (F(26,80) = 5.70, p < 0.001; Wilk’s Λ = 0.123, partial η² = 0.649). Fisher’s least squares difference post hoc analyses showed that participants in the Skill group had greater JH, CMJ-NPP, reactive strength, and running GRF values versus Big players but not Big–skill players (p < 0.05). Big athletes had greater BF%, LBM, total body mass, and GCT values than Skill and Big–skill athletes (p < 0.05). Big–skill players had greater GCT asymmetry than Skill and Big players (p < 0.05). Asymmetries in running forces, CMJ eccentric, and concentric IMP were not different (p > 0.05). Morphological and performance biomechanics differences are pronounced between Skill and Big players. Big–skill players possess characteristics from both groups. Laboratory-derived metrics offer precise values of running and jumping force strategies and body composition that can aid sports science researchers and practitioners in refining draft trainee profiles. Full article

Despite their relatively small numbers, large old trees play disproportionately important roles in global biodiversity and ecosystem functions. There is a lack of systematic reviews and quantitative analyses of the accumulated literature. Understanding the research context and evolution could pump prime research and conservation endeavors. Using the comprehensive Web of Science, we applied VOSviewer (1.6.19) and CiteSpace (6.1R2) bibliometric software to examine the large old tree research field in 2000–2022. The queries of the bibliographic database generated quantitative–visual depictions in the form of knowledge maps. The nodes denote research intensity, and inter-node linkages denote the pathways and frequencies of collaborative activities. The research outputs differed significantly in terms of regions, countries, institutions, high-citation articles, productive researchers, hot topics, and research frontiers. Conspicuous spatial disparities were displayed, with the U.S.A., China, and Australia leading in publication counts and a cluster of European countries making considerable collective contributions. The research collaboration demonstrated a dichotomy: European countries networked more by geographical propinquity, and the top three countries connected by long-distance leap-frog jumps. The entrenched discrepancies between the endowed developed domains vis-à-vis the deprived developing domains were clearly expressed. The research productivity progressed through three stages: initial, growth, and flourishing. The leading institutions, researchers, and highly cited papers were recognized. The keyword analysis pinpointed diverse research hotspots: growth dynamics, conservation and management, ecological functions, and environmental response. This study informs recommendations for future research directions and cooperation on longevity mechanisms, evolutionary adaptation, dynamic monitoring, and temporal–spatial patterns. The integrated application of GIS, machine learning, and big data technologies could strengthen research capability. Full article

In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods. Full article

Advances in sequencing technologies and the bioinformatic analysis of big data facilitate the study of jumping genes’ activity in the human genome in cancer from a broad perspective. Retrotransposons, which move from one genomic site to another by a copy-and-paste mechanism, are regulated by various molecular pathways that may be disrupted during tumorigenesis. Active retrotransposons can stimulate type I IFN responses. Although accumulated evidence suggests that retrotransposons can induce inflammation, the research investigating the exact mechanism of triggering these responses is ongoing. Understanding these mechanisms could improve the therapeutic management of cancer through the use of retrotransposon-induced inflammation as a tool to instigate immune responses to tumors. Full article

To solve the research–practice gap and take one step forward toward using big data with real-world evidence, the present study aims to adopt a novel method using machine learning to pool findings from meta-analyses and predict the change of countermovement jump. The data were collected through a total of 124 individual studies included in 16 recent meta-analyses. The performance of four selected machine learning algorithms including support vector machine, random forest (RF) ensemble, light gradient boosted machine, and the neural network using multi-layer perceptron was compared. The RF yielded the highest accuracy (mean absolute error: 0.071 cm; R²: 0.985). Based on the feature importance calculated by the RF regressor, the baseline CMJ (“Pre-CMJ”) was the most impactful predictor, followed by age (“Age”), the total number of training sessions received (“Total number of training_session”), controlled or non-controlled conditions (“Control (no training)”), whether the training program included squat, lunge, deadlift, or hip thrust exercises (“Squat_Lunge_Deadlift_Hipthrust_True”, “Squat_Lunge_Deadlift_Hipthrust_False”), or “Plyometric (mixed fast/slow SSC)”, and whether the athlete was from an Asian pacific region including Australia (“Race_Asian or Australian”). By using multiple simulated virtual cases, the successful predictions of the CMJ improvement are shown, whereas the perceived benefits and limitations of using machine learning in a meta-analysis are discussed. Full article

Absolute sea-level rise has become an important topic globally due to climate change. In addition, relative sea-level rise due to the vertical land motion in coastal areas can have a big societal impact. Vertical land motion (VLM) in Southeast Asia includes a tectonically induced component: uplift and subsidence in plate boundary zones where both Peninsular and East Malaysia are located. In this paper, the relative sea-level trends and (seismic cycle-induced) temporal changes across Malaysia were investigated. To do so, the data (1984–2019) from 21 tide gauges were analyzed, along with a subset (1994–2021) of nearby Malaysian GNSS stations. Changes in absolute sea level (ASL) at these locations (1992–2021) were also estimated from satellite altimetry data. As a first for Peninsular and East Malaysia, the combination ASL minus VLM was robustly used to validate relative sea-level rise from tide-gauge data and provide relative sea-level trend estimates based on a common data period of 25+ years. A good match between both the remote and in situ sea-level rise estimations was observed, especially for Peninsular Malaysia (differences < 1 mm/year), when split trends were estimated from the tide gauges and GNSS time series to distinguish between the different VLM regimes that exist due to the 2004 Sumatra–Andaman megathrust earthquake. As in the south of Thailand, post-seismic-induced negative VLM has increased relative sea-level rise by 2–3 mm/year along the Andaman Sea and Malacca Strait coastlines since 2005. For East Malaysia, the validation shows higher differences (bias of 2–3 mm/year), but this poorer match is significantly improved by either not including data after 1 January 2014 or applying a generic jump to all East Malay tide gauges from that date onwards. Overall, the present relative sea-level trends range from 4 to 6 mm/year for Malaysia with a few regions showing up to 9 mm/year due to human-induced land subsidence. Full article