Several physiological and pathological processes, such as development, wound healing, and cancer invasion, depend on cell migration through fibrous extracellular matrix (ECM). In such contexts, topographical features of the ECM, including fiber alignment and pore size, strongly bias migration, a phenomenon known as topotaxis. To explore this guidance mechanism in a controlled theoretical setting, we present a minimal particle-based model of single-cell motility in two-dimensional environments abstracted as networks of elongated obstacles. This abstraction captures key geometric and topographical constraints of fibrous microenvironments while remaining computationally tractable. Our framework integrates chemotactic bias, stochastic polarity dynamics, steric repulsion from obstacles, escape strategies from mechanical trapping, and minimal remodeling of the obstacles network. Adaptive polarity perturbations mimic active cellular responses such as invadopodial protrusion or random reorientation, while a displacement-based criterion detects trapping events. Heterogeneity is incorporated by assigning variable repulsion strengths to obstacles, and remodeling is implemented by allowing local displacements induced by cell-obstacle contact. Simulation results show that active remodeling of obstacles consistently enhances migration efficiency and target acquisition, whereas escape strategies alone provide only partial improvement, and heterogeneity introduces directional variability. At long timescales, trajectories converge toward effective diffusion, but intermediate dynamics display nontrivial deviations due to confinement and obstacle interactions, highlighting a topotaxis-driven component of motility. Overall, this work positions cell migration within the theoretical context of obstacles networks, providing mechanistic insight into how confinement, anomalous transport, and remodeling interact to shape directional migration. While simplified to two dimensions and lacking entanglement effects characteristic of real three-dimensional ECMs, the model offers a tractable and extensible framework for future studies, including the incorporation of cell deformations or more realistic ECM architectures.

In flight tests, to meet the requirements of consistent acquisition and storage of multiple targets, multiple systems, and multiple data types, various data types are processed into Pulse Code Modulation (PCM) data streams using PCM encoding for storage. Aiming at the requirement of real-time storage of high-bit-rate PCM data streams, a large-capacity storage system based on Serial Advanced Technology Attachment 3.0 (SATA3.0) is designed. The system uses the Kintex 7 series Field-Programmable Gate Array (FPGA) as the control core, receives PCM data streams through the Low-Voltage Differential Signaling (LVDS) low-voltage differential interface, stores the received PCM data streams into the mSATA disk via the SATA3.0 transmission bus, and transmits the stored data back to the host computer through the USB3.0 interface for analysis. Meanwhile, to solve the problem of complex data export, the storage system constructs a FAT32 file system through the MicroBlaze soft core to optimize the management and operation of the large-capacity storage system. Test results show that the storage system can perform stable high-rate storage at −40 °C~80 °C.

Considering the lack of evaluation of the human factors of 2D touch input devices in VR, this paper explores the impact of touch device size and input mechanism on target selection performance from an ergonomic perspective. Through pilot experiments, two key factors, “touch device size” and “input mechanism,” were identified, and a VR interaction prototype based on smartphones and RGB-D fingertip tracking was constructed. In the formal experiment, we evaluated three sizes (3-inch, 5-inch, 7-inch) and three mechanisms (touch, release, lift-and-tap) across three representative tasks (target acquisition, teleportation navigation, text entry), measuring completion time, error rate, and NASA-TLX. Results demonstrated the 5-inch size paired with the release mechanism achieved the best tradeoff. Combined with Fitts’s law and the NTMR model, advantages in thumb comfort zone, C-D ratio matching and feedback rhythm were revealed. The study provided an empirical basis and engineering suggestions for 2D touch input in VR.

Screen-guided training is a widely used method for calibrating myoelectric prostheses, wherein users follow visual prompts. However, this approach often fails to capture the complexities of real-world usage when the user is actively engaging with the controller. This study, therefore, aimed to develop an alternative training protocol that promotes more robust pattern recognition-based myoelectric control. In an experiment with 20 participants, we compared three training methods: conventional screen-guided training without feedback, real-time visual feedback of principal component analysis (PCA)-based projections of EMG activity, and real-time classification feedback with intentionally corrupted classifier outputs. After training, participants completed a Fitts' law-style target acquisition task in a virtual environment, repeating it at three different difficulty levels. We then evaluated how offline accuracy and metrics, particularly Bhattacharyya Distances computed from combinations of the PCA projections, correlated with online control performance. Our findings indicate that training with feedback yielded the best performance, with PCA-based visual feedback providing the most effective calibration environment. Additionally, projecting the EMG data collected with PCA-based feedback into the PCA space derived from the no-feedback data improved the correlation between offline separability metrics and the online Fitts' Law throughput. Interestingly, this correlation was stronger for the easy difficulty level. Nevertheless, the benefits of PCA-based feedback were consistent across the three different difficulty levels of the Fitts' law task, it as a beneficial and robust approach worthy of further exploration.

Parallel light detection and ranging (LiDAR) is widely adopted for its low computational burden and rapid three-dimensional (3D) reconstruction. Yet, it remains constrained by inter-channel crosstalk and limited long-distance performance. Here, we introduce a spectrally encoded parallel LiDAR based on super-bunching light, exhibiting negligible cross-correlation between wavelengths, enabling quasi-orthogonal channel division without crosstalk. This approach supports robust parallel ranging, rapid and accurate 3D reconstruction, and effective target classification. Our scheme achieves high-precision ranging with errors as low as 4 mm and can detect targets moving at velocities as low as 5 mm/s. It further enables reliable ranging and 3D imaging beyond 40 m, with exceptional anti-interference performance, even when noise exceeds the echo signal by three orders of magnitude. Combining high precision, sensitivity, long-range detection, dynamic target acquisition, precise 3D reconstruction, and robust anti-interference, our LiDAR offers significant potential for enhancing environmental perception technologies.

Due to the limited number of SAR samples in the dataset, current networks for SAR automatic target recognition (SAR ATR) are prone to overfitting the environmental information, which diminishes their generalization ability under cross-background conditions. However, acquiring sufficient measured data to cover the entire environmental space remains a significant challenge. This paper proposes a novel feature disentanglement network, named FDSANet. The network is designed to decouple and distinguish the features of the target from the background before classification, thereby improving its adaptability to background changes. Specifically, the network consists of two sub-networks. The first is an autoencoder sub-network based on dual-mask-guided slot attention. This sub-network utilizes target mask to guide the encoder to distinguish between target and background features. It then outputs these features as independent representations, respectively, achieving feature disentanglement. The second is a classification sub-network. It includes an encoder and a classifier, which work together to perform the classification based on the extracted target features. This network enhances the causal relationship between the target and the classification result, while mitigating the background’s interference on the classification. Moreover, the network, trained under a fixed background, demonstrates strong adaptability when applied to a new background. Experiments conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, as well as the OpenSARShip dataset, demonstrate the superior performance of FDSANet.

Mixed Reality devices, now as ubiquitous as smartphones and laptops, are increasingly utilised in everyday scenarios. However, real-world use often involves challenging environmental factors such as ambient noise, which can adversely affect user interaction with MR systems. This study investigates the impact of three types of ambient noise—music, urban noise and speech—on MR interaction. We constructed Bayesian regression models to assess movement time, pointing offset, error rate and throughput on target acquisition task, and throughput, uncorrected error rate, corrected error rate, and words per minute on text entry task under different noise conditions. Our results indicated that meaningless speech reduced text-entry throughput by 5.36%, fast-tempo music increased movement time by 4.44%, slow-tempo music increased pointing offset by 4.07%, urban indoor noise increased typing throughput by 3.33% and urban outdoor noise decreased throughput by 2.74%. These findings demonstrate how ambient noise affects MR performance, advancing our understanding of situational impairments in MR. We propose strategies for designing noise-resilient MR interfaces to enhance usability in dynamic environments.

Purpose: Economic uncertainty affects mergers and acquisitions (M&As); however, there is limited evidence regarding its effect on the propensity to acquire privately held versus publicly traded companies, as well as how institutional factors influence such decisions. I hypothesize that economic uncertainty and financial market devel-opment influence the preferences for the type of acquisition targets. Design/methodology/approach: Using a sample of European M&A transactions and employing both measures of economic uncertainty and the uncertainty spike re-sulting from the pandemic, as well as the degree of financial market development, I test whether these factors are associated with the frequency of acquisitions involving privately held companies, further distinguishing between stand-alone private firms and subsidiaries. Findings: My analysis indicates that, under market uncertainty, acquisitions of pub-licly traded company diminish, while transactions involving privately held compa-nies increase. Furthermore, I demonstrate that the COVID-related spike in uncer-tainty generates an additional effect beyond general economic uncertainty. I also find that the positive association between uncertainty and acquisitions of privately held companies particularly applies to stand-alone private firms rather than subsidiaries. My findings also reveal that acquisitions of private companies are more likely in financially developed markets. Originality/value: This study contributes to the literature on the effects of economic uncertainty on M&As by highlighting the importance of uncertainty in shaping the relative frequency of acquisitions involving private versus public companies, while also emphasizing the significance of institutional factors in influencing such prefer ences. Moreover, my research enhances the understanding of privately held firms, whose specificities have largely been neglected in the academic debate.

Large-format touchscreens have become commonplace in classrooms and meeting rooms, yet little is known about how their scale affects input behavior. Operating these displays engages more upper-limb joints—especially the shoulder—thereby altering movement dynamics. In a 65-inch touchscreen study, we found that conventional Fitts' law explains ≤ 80% of the variance in movement time, as performance is primarily driven by amplitude. To address the modeling bias resulting from this amplitude dominance, we refine the index of difficulty as IDx = log_2(A/(W + c) + 1). Rather than treating c as a mere fitting parameter, we interpret it as an intrinsic property of pointing dynamics: it quantifies the systematic deviation from the canonical speed-accuracy tradeoff implied by the "as quickly and accurately as possible" instruction. By calibrating c with the proposed anti-overfitting criteria, we improve model fits for both finger and pen input, raising R² to above 0.97. We present the calibration rules, interpret c across contexts, validate IDx robustness via leave-one-out cross-validation, and demonstrate its generalizability on data from prior studies—including a large tabletop experiment. Finally, we translate the findings into practical guidelines for UI and experimental design.

Detecting and tracking non-cooperative targets in sequential star sensor images poses several challenges in rapid expanding space involving mega constellations, commercial space activities: poor real-time performance in target acquisition, inadequate generalization to different target speeds, and dependence on the star sensor’s attitude priors. To address these challenges, an improved multi-target tracking deep learning model based on the CenterTrack model is proposed in this paper. A sophisticated training and testing dataset that closely replicates real on-orbit star sensor images is constructed. By aggregating features across the entire sequence and enhancing the target identification ability from background noise, an improved tracking accuracy is realized. These improvements reduce false positive rates by approximately 60% and lower true target miss rates by 20% compared to the baseline original CenterTrack model. Furthermore, adjusting hyperparameters and optimizing the tracking algorithm reduces target ID switching frequency by approximately 50%. Compared to the traditional algorithm, the improved model can accurately capture newly appearing targets using only two frames, achieving a six-fold speed improvement. The generalization performance is significantly improved with respect to variations in target morphology and velocity, thus a higher target speed tolerance is achieved. The proposed model eliminates the requirement of external attitude priors, thereby enhancing its robustness, and shows significant potential for emerging on-orbit non-cooperative target tracking applications.

To increase target acquisition probability and the signal-to-noise ratio (SNR) of hyperspectral images, this paper presents a wide-field, dual-slit, low-distortion, and high-sensitivity Offner hyperspectral imager, with a wavelength range of 0.4 μm to 0.9 μm, a numerical aperture of 0.15, and a slit length of 73 mm. To avoid signal aliasing, the space between the dual slits is 2.4 mm, increasing the SNR by 1.4 times after dual-slit image fusion. Furthermore, to achieve the required registration accuracy of dual-slit images, the spectral performance of the hyperspectral imager is critical. Thus, we compensate and correct the spectral performance and dispersion nonlinearity of the hyperspectral imager by taking advantages of the material properties and tilt eccentricity of a low-dispersion internal reflection curved prism and high-dispersion double-pass curved prisms. To meet the final operation requirements, the tilt of the internal reflection curved prism is used as a compensator. Using the modulation transfer function (MTF) as the evaluation criterion, an inverse sensitivity analysis confirmed that the compensator is a highly sensitive component. Additionally, the root mean square standard deviation (RSS) discrete calculation method was adopted to assess the influence of actual assembly tolerance on spectral performance. The test results demonstrate that the hyperspectral imager meets the registration accuracy requirements of dual-slit images, with an MTF better than 0.4. Furthermore, the spectral smile and spectral keystone of the dual-slit images are both less than or equal to 0.3 pixels.

The U.S. military is procuring systems for complex joint fighting, requiring novel teaming approaches that maximize the capabilities of humans, artificial intelligence (AI), and autonomy. This case study describes a research effort to support aerial reconnaissance and target acquisition with crewed and uncrewed platforms. This effort integrated human factors with cutting-edge AI techniques, using Decision-Centered Design to identify requirements of the reconnaissance work and multiple AI development approaches. Lessons learned include the following. First, the team applied Roth and colleagues’ macrocognitive synthesized framework of decision making. Its emphasis on integrated human and AI sensemaking capabilities, as well as interfaces to facilitate common ground, facilitated cross-discipline collaboration. Second, the team collaborated on the goals and data of underlying AI features in parallel with human-AI interaction development. Third, scenario-based design was valuable to facilitate co-design activities. Embedding cognitive requirements within scenario storyboards made complex military work accessible to the full engineering team.

ABSTRACT We propose a target detection algorithm for K -distributed Synthetic Aperture Radar (SAR) clutter data. We combine texture estimation and constant false alarm rate (CFAR) detection through a maximum a posteriori (MAP) estimator for the texture in intensity format. The CFAR detector determines the threshold for Γ -distributed background clutter texture. We provide a closed-form expression for the CFAR detection threshold. Furthermore, a closed-form expression for the detection probability has been derived for the proposed CFAR detector. Analytical results are presented to assess the CFAR detection performance. We further assess the effectiveness of the CFAR detector on Moving and Stationary Target Acquisition and Recognition (MSTAR) data. Experimental results illustrate the computational effectiveness of the proposed detector over conventional CFAR- K detectors while attaining improved detection accuracy compared to the CFAR-WBL, CFAR-LGN, and existing CFAR- K detectors.

This study develops a comprehensive selection model framework for acquiring target companies, with a specific focus on PT Sincerity Investama Abadi in Jakarta, Indonesia. The research integrates quantitative valuation methods, including Discounted Cash Flow (DCF) and the Adjusted Net Asset Method (ANAM), with financial ratio analysis to support strategic acquisition decisions. Through the systematic evaluation of five SME candidates using a weighted scoring model, the study identifies the most viable investment opportunities based on financial performance, management readiness, and strategic fit. The findings demonstrate that PT Multindo Cipta Kreasi emerges as the primary acquisition target, with a combined valuation of IDR 2.48 billion and a projected IRR of 15.69%, followed by Omen Futsal as a secondary diversification option. The proposed framework provides private equity and investment firms with a practical tool for optimizing acquisition choices, mitigating risks, and enhancing value creation. The study offers strategic implementation recommendations to improve the selection process and support sustainable investment decisions in the SME sector.

Abstract: Several cases have been reported to the police since 2022, where at gunpoint, criminals would storm into the church building in the middle of a sermon and demand congregants to hand over their electronic gadgets and cash. This traumatic experience has, in some cases, resulted in the loss of lives. The question that this article interrogates is why criminals are targeting Pentecostal churches. Literature reveals that most Pentecostal churches in urban areas, where this criminal practice is prevalent, have an affluent membership. This affluence is mainly demonstrated by their flamboyant cars parked in the church’s parking lot and, in some cases, their expensive dress code. Another attraction to Pentecostals by criminals is the recent media attention on controversial Pentecostal pastors who extort money from their members, which creates an impression that Pentecostal churches are an easy target for quick cash acquisition. A literary analysis using relevant case studies will be engaged to demonstrate how such violent crimes negatively impact the mission of Pentecostals. The study’s objective is to provide a solution on how Pentecostal churches can protect themselves against this violent phenomenon that threatens their mission in communities. Kritzinger’s theoretical framework of missiological encounterology, buttressed by the Spiral Dynamics theory, will be used to describe how Pentecostals, as agents of God’s mission, encounter violence caused by the context of poverty, drawing from the ecclesiological practices of other Pentecostals in East and West Africa who have learnt how to do mission within the context of security vulnerabilities. This framework will further assist Pentecostals to reflect on their theology and spirituality in the context of security vulnerability and help them develop practical action points to help them maintain the mission in their daily reflections. Contribution: One of those pragmatic outcomes is for Pentecostal churches to hold dialogues among themselves and with strategic stakeholders such as private security companies, including the South African Police Services (SAPS) and its community policing forums, in protecting the mission of the gospel within vulnerable communities.

Abstract Introduction Vestibular ocular motor (VOM) functions, particularly antisaccades (moving eyes away from visual stimuli, requiring reflexive response inhibition), are associated with sleepiness and performance in healthy sleepers under rested and sleep deprived conditions. The effect of sleep disorders on antisaccadic function at rested or sleep deprived states is unknown. We compared antisaccadic function in people young adults with healthy sleep (HS) and individuals with sleep disorders (SD) at rested baseline and following extended wakefulness. Methods Following baseline sleep, participants completed a ~ 29-hour extended wakefulness. VOM antisaccades (NeuroFlex®) were assessed 5 times during rested (≤13hrs awake) and sleep deprived (≥17hrs awake) states. Antisaccade measures were mean gaze vergence (difference in left vs right eye gaze direction, important for target focusing), mean gaze latency (delay between stimulus presentation and eyes on target) acquisition error (degrees between eyes and target), and directional accuracy (successful eye tracking direction). Linear mixed models compared groups (HS vs SD) and state (rested vs sleep deprived) and their interaction. Results 84 participants (29 ± 10yrs) completed the protocol. There was a main effect of group (HS vs. SD) for mean vergence (-0.02 vs. 0.97 degrees, F(1,85.95) = 4.29,p=.041) and a main effect of state (rested vs sleep deprived) for mean latency (524.86 vs. 451.67ms, F(1,67.88) = 38.06,p<.001) and directional accuracy (62.78 vs. 71.08 degrees, F(1,69.54) = 28.11,p<.001). No other main or interaction effects were observed. Discussion Antisaccadic function is differentially affected by disordered sleep and sleep loss. However, there is no apparent worsening of function in sleep disordered individuals with sleep loss relative to health sleepers.

Self-supervised representation learning with continuous training data improves the feel and performance of myoelectric control.

The unsteady and discontinuous liquid flow in the microchannel affects the efficiency of sample mixing, molecular detection, target acquisition, and biochemical reaction. In this work, an active method of reducing the flow pulsation in the microchannel of a pneumatic microfluidic chip is proposed by using an on-chip membrane microvalve as a valve chamber damping hole or a valve chamber accumulator. The structure, working principle, and multi-physical model of the reducing element of reducing the flow pulsation in a microchannel are presented. When the flow pulsation in the microchannel is sinusoidal, square wave, or pulse, the simulation effect of flow pulsation reduction is given when the membrane valve has different permutations and combinations. The experimental results show that the inlet flow of the reducing element is a square wave pulsation with an amplitude of 0.1 mL/s and a period of 2 s, the outlet flow of the reducing element is assisted by 0.017 and the fluctuation frequency is accompanied by a decrease. The test data and simulation results verify the rationality of the flow reduction element in the membrane valve microchannel, the correctness of the theoretical model, and the practicability of the specific application, which provides a higher precision automatic control technology for the microfluidic chip with high integration and complex reaction function.

This article presents a comprehensive analysis of the implementation of innovative information and analytical solutions within standardized combat management processes, focusing on the real-life combat experience of the 13th Operational Brigade of the National Guard of Ukraine. The authors examine the profound transformation of classical command-and-control models that still bear the influence of the Soviet legacy. Key systemic limitations are highlighted, including excessive bureaucratization, manual planning, the lack of modern process automation, and a deficiency of tools for operational data analysis. The study explores practical cases of integrating digital platforms into combat operations: the use of the “Delta” situational awareness system for real-time monitoring and visualization of intelligence data; and the application of Power BI for processing and interpreting large volumes of combat-related information, enabling data-driven decision-making in compressed timeframes. Special attention is paid to the emergence of new organizational approaches driven by the realities of modern warfare – particularly the introduction of ISTAR units (Intelligence, Surveillance, Target Acquisition and Reconnaissance) and the role of the Battle Captain. These roles play a critical part in synchronizing intelligence, command, and firepower in real time, significantly enhancing the responsiveness to dynamic battlefield conditions. The article is practical in nature, drawing upon empirical data collected during the deployment of a joint operational group in active combat conditions. This allows the authors to formulate specific recommendations for scaling the successful organizational model across other units within the Ukrainian Defense Forces. The experience of the 13th Brigade is presented as a grassroots institutional initiative, where digital technologies, data analytics, and elements of civilian management lay the foundation for a modern, flexible, and adaptive combat management system. The proposed approaches hold strong potential for regulatory formalization and further digital transformation of command-and-control structures, which is critically important in the context of protracted armed conflict and the need to strengthen operational capabilities

Abstract This paper presents the design and implementation of a teleoperation system for a biped wall-climbing robot. A comprehensive kinematic model is developed to support two control modes: position–position (PP) and position–velocity (PV). The control diagram integrates the human operator’s input with real-time feedback from the climbing robot, enabling remote motion control and environmental interaction. A teleoperation device is employed to realize the robot’s motion control. Additionally, the system utilizes markers for visual localization and remote target acquisition, facilitating semi-autonomous navigation during teleoperation. Experimental results demonstrate that the proposed teleoperation system accurately reproduces the intended climbing motions and effectively supports user-guided wall traversal, validating the designed control strategies.