Varying Lighting Conditions Research Articles

Identification and Expression Analysis of TCP Transcription Factors Under Abiotic Stress in Phoebe bournei

The TCP gene family encodes plant transcription factors crucial for regulating growth and development. While TCP genes have been identified in various species, they have not been studied in Phoebe bournei (Hemsl.). This study identified 29 TCP genes in the P. bournei genome, categorizing them into Class I (PCF) and Class II (CYC/TB1 and CIN). We conducted analyses on the PbTCP gene at both the protein level (physicochemical properties) and the gene sequence level (subcellular localization, chromosomal distribution, phylogenetic relationships, conserved motifs, and gene structure). Most P. bournei TCP genes are localized in the nucleus, except PbTCP9 in the mitochondria and PbTCP8 in both the chloroplast and nucleus. Chromosomal mapping showed 29 TCP genes unevenly distributed across 10 chromosomes, except chromosome 8 and 9. We also analyzed the promoter cis-regulatory elements, which are mainly involved in plant growth and development and hormone responses. Notably, most PbTCP transcription factors respond highly to light. Further analysis revealed three subfamily genes expressed in five P. bournei tissues: leaves, root bark, root xylem, stem xylem, and stem bark, with predominant PCF genes. Using qRT-PCR, we examined six representative genes—PbTCP16, PbTCP23, PbTCP7, PbTCP29, PbTCP14, and PbTCP15—under stress conditions such as high temperature, drought, light exposure, and dark. PbTCP14 and PbTCP15 showed significantly higher expression under heat, drought, light and dark stress. We hypothesize that TCP transcription factors play a key role in growth under varying light conditions, possibly mediated by auxin hormones. This work provides insights into the TCP gene family’s functional characteristics and stress resistance regulation in P. bournei.

Unique structural attributes of the PSI-NDH supercomplex in Physcomitrium patens.

Cyclic electron transport around photosystem I (PSI) is essential for the protection of the photosynthetic apparatus in plants under diverse light conditions. This process is primarily mediated by Proton Gradient Regulation 5 protein/Proton Gradient Regulation 5-like photosynthetic phenotype 1 protein (PGR5/PGRL1) and NADH dehydrogenase-like complex (NDH). In angiosperms, NDH interacts with two PSI complexes through distinct monomeric antennae, LHCA5 and LHCA6, which is crucial for its higher stability under variable light conditions. This interaction represents an advanced evolutionary stage and offers limited insight into the origin of the PSI-NDH supercomplex in evolutionarily older organisms. In contrast, the moss Physcomitrium patens (Pp), which retains the lhca5 gene but lacks the lhca6, offers a glimpse into an earlier evolutionary stage of the PSI-NDH supercomplex. Here we present structural evidence of the Pp PSI-NDH supercomplex formation by single particle electron microscopy, demonstrating the unique ability of Pp to bind a single PSI in two different configurations. One configuration closely resembles the angiosperm model, whereas the other exhibits a novel PSI orientation, rotated clockwise. This structural flexibility in Pp is presumably enabled by the variable incorporation of LHCA5 within PSI and is indicative of an early evolutionary adaptation that allowed for greater diversity at the PSI-NDH interface. Our findings suggest that this variability was reduced as the structural complexity of the NDH complex increased in vascular plants, primarily angiosperms. This study not only clarifies the evolutionary development of PSI-NDH supercomplexes but also highlights the dynamic nature of the adaptive mechanisms of plant photosynthesis.

Spiking Neural Networks for Real-Time Pedestrian Street-Crossing Detection Using Dynamic Vision Sensors in Simulated Adverse Weather Conditions

This study explores the integration of Spiking Neural Networks (SNNs) with Dynamic Vision Sensors (DVSs) to enhance pedestrian street-crossing detection in adverse weather conditions—a critical challenge for autonomous vehicle systems. Utilizing the high temporal resolution and low latency of DVSs, which excel in dynamic, low-light, and high-contrast environments, this research evaluates the effectiveness of SNNs compared to traditional Convolutional Neural Networks (CNNs). The experimental setup involved a custom dataset from the CARLA simulator, designed to mimic real-world variability, including rain, fog, and varying lighting conditions. Additionally, the JAAD dataset was adopted to allow for evaluations using real-world data. The SNN models were optimized using Temporally Effective Batch Normalization (TEBN) and benchmarked against well-established deep learning models, concerning their accuracy, computational efficiency, and energy efficiency in complex weather conditions. This study also conducted a comprehensive analysis of energy consumption, highlighting the significant reduction in energy usage achieved by SNNs when processing DVS data. The results indicate that SNNs, when integrated with DVSs, not only reduce computational overhead but also dramatically lower energy consumption, making them a highly efficient choice for real-time applications in autonomous vehicles (AVs).

MoiréTag: A Low-Cost Tag for High-Precision Tangible Interactions without Active Components

In this paper, we present MoiréTag—a novel tag-like device that magnifies displacement without active components for indirect sensing of subtle tangible interactions. The device consists of two overlapping layers of stripe patterns with distinct pattern frequencies. These layers create Moiré fringes that can move faster than the actual movement of a layer. Using a customized image processing pipeline, we show that MoiréTag can reliably detect sub-mm movement in real-time (mean error = 0.043 mm) under varying lighting conditions, camera angles, and camera distances. We also demonstrate five applications of MoiréTag to showcase its potential as a low-cost solution to capture and monitor small changes in movement and other physical properties, such as force and volume, by converting them into displacement.

PcMNet: An efficient lightweight apple detection algorithm in natural orchards

Apple detection plays a critical role in enabling the functionality of harvesting robots within natural orchard environments. To address challenges related to low detection accuracy, slow inference speed, and high parameter count, we present PcMNet, a lightweight detection model based on an improved YOLOv8 network. Initially, we employed Partial Convolution (Pconv) to construct a PR module, forming the Pconv-block, which subsequently replaced the original C2f feature extraction module within the YOLOv8n backbone. This replacement led to improvements in both detection accuracy and speed, while simultaneously reducing computational complexity (FLOPs), parameter count, and model size. Furthermore, the Cross-Scale Feature Fusion (CCFF) module was refined into Faster-Cross-Scale Feature Fusion (Faster-CCFF) with the integration of Pconv-block, significantly enhancing the model's feature extraction and fusion capabilities. Additionally, we introduced Mixed Local Channel Attention (MLCA) to further strengthen the model's capacity to capture essential features while effectively suppressing background noise. Experimental results demonstrate that PcMNet achieved a detection accuracy of 92.8 % and an mAP@0.5 of 95.5 %, representing improvements of 1.4 and 0.7 percentage points, respectively, over YOLOv8n. Moreover, PcMNet successfully reduced FLOPs, parameter count, and model size to 5.1 G, 1.4 M, and 3.2 MB, respectively. The per-image detection time was reduced to 2.3 ms, indicating reductions of 37.80 %, 53.33 %, 49.21 %, and 56.60 % in FLOPs, parameters, model size, and detection time compared to YOLOv8n. When deployed on edge computing devices with TensorRT acceleration, PcMNet achieved a detection rate of 92 FPS. Field validation experiments conducted in natural orchard environments confirmed PcMNet's superior ability to detect apples under challenging conditions, such as occlusions and varying lighting conditions. Its lightweight design and rapid detection capabilities provide a valuable reference for achieving real-time apple detection in automated and intelligent harvesting robots, thereby contributing to advancements in smart agriculture.

Na+-driven pH regulation by Na+/H+ antiporters promotes photosynthetic efficiency in cyanobacteria.

Photosynthetic organisms have developed mechanisms to regulate light reactions in response to varying light conditions. Photosynthetic electron transport leads to the formation of a ΔpH across the thylakoid membrane, which is crucial for regulating electron transport. However, other pH modulators remain to be identified, particularly in cyanobacteria. In this study, we evaluated the potential involvement of six Na+/H+ antiporters (NhaS1-NhaS6) in control of pH in the cyanobacterium Synechocystis sp. PCC 6803. Synechocystis showed a strong requirement for Na+ at high light intensities, with ΔnhaS1 and ΔnhaS2 strains unable to grow under high light conditions. We analyzed Na+ efflux-driven H+-uptake activities of NhaS1-NhaS6 in inverted membranes of Escherichia coli. Biological fractionation and immunoelectron microscopy revealed that NhaS1 localizes to both the plasma and thylakoid membranes while NhaS2 localizes to the plasma membrane. Measurement of photosynthesis activity indicated that NhaS2 promotes ATP production and electron transport from PQ to P700. Measurements of pH outside of the cells and in the cytoplasm suggested that both NhaS1 and NhaS2 are involved in plasma membrane-mediated light-dependent H+ uptake and cytoplasmic acidification. NhaS1 and NhaS2 were also found to prevent photoinhibition under high light treatment. These results indicate that H+ transport mediated by NhaS1 and NhaS2 plays a role in regulating intracellular pH and maintaining photosynthetic electron transport.

Exploring patterns in older pedestrian involved crashes during nighttime

Nighttime crashes involving older pedestrians pose a significant safety concern due to their age-related vulnerabilities such as reduced vision and slower reaction times. This study analyzes crash data from Texas for six years (2017–2022) using Association Rules Mining (ARM) to identify patterns and associations affecting crash severity for older pedestrians aged 65–74 years and those over 74 years under varying lighting conditions. The findings reveal that high-speed limits and complex road environments significantly increase the risk of fatal or severe injuries for both age groups, particularly under inadequate lighting. Additionally, demographic factors, adverse weather conditions, and specific road features further influence crash outcomes. These insights highlight the need for interventions, including lower speed limits, enhanced street lighting, and the implementation of advanced technologies such as modern pedestrian detection systems, sensor technology, pedestrian bags, accessible pedestrian signals, to improve the safety of older pedestrians. Policymakers should leverage these insights to formulate strategies that improve road safety for older pedestrians, addressing their unique vulnerabilities in various nighttime conditions.

Improving solar panel performance using MPPT optimization algorithms

One of the primary challenges faced by solar energy systems is the issue of inconsistent lighting and uneven distribution of sunlight across the surface of solar panels. These conditions can result in a substantial reduction in the power output of the panels and, in some cases, the complete failure of certain solar cells. This paper examines the effects of shading on solar panel performance, focusing on how partial shading impacts energy production and efficiency through both experimental analysis and simulation. The findings emphasize the critical influence of shading patterns and configurations on the overall efficiency of photovoltaic systems. The study highlights the necessity of implementing shading mitigation strategies and optimization algorithms to enhance energy production and ensure the reliability of solar systems operating in environments with varying light conditions. This study showed that the use of optimization algorithms in photovoltaic systems exposed to shading increases their efficiency in terms of energy saving as well as performance stability. The simulation results shows that the GWO algorithm gives a higher performance than the other algorithms in the same partial shading conditions.

The HAT1 transcription factor regulates photomorphogenesis and skotomorphogenesis via phytohormone levels.

Plants dynamically modulate their growth and development to acclimate to the fluctuating light environment via a complex phytohormone network. However, the dynamic molecular regulatory mechanisms underlying how plants regulate phytohormones during skotomorphogenesis and photomorphogenesis are largely unknown. Here, we identified a HD-ZIP II transcription factor, HOMEODOMAIN ARABIDOPSIS THALIANA1 (HAT1), as a key node that modulates the dose effects of brassinosteroids (BR) and auxin on hypocotyl growth during skotomorphogenesis and photomorphogenesis. Compared with the wild-type (Col-0), both HAT1 loss of function and its overexpression led to disrupted photomorphogenic and skotomorphogenic hypocotyl growth. HAT1 overexpression (HAT1OX) plants displayed longer hypocotyls in the light but shorter hypocotyls in darkness, whereas the triple mutant hat1hat2hat3 showed the opposite phenotype. Furthermore, we found that CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1) interacted with dephosphorylated HAT1 and facilitated the degradation of HAT1 by ubiquitination in darkness, while HAT1 was phosphorylated and stabilized by BRASSINOSTEROID INSENSITIVE2 (BIN2) in the light. Interestingly, we observed distinct dose-dependent effects of BR and auxin on hypocotyl elongation under varying light conditions and that HAT1 functioned as a key node in this process. The shorter hypocotyl of HAT1OX in darkness was due to the inhibition of BR biosynthetic gene BRASSINOSTEROID-6-OXIDASE2 (BR6OX2) expression to reduce BRs content, while brassinolide (BL) treatment alleviated this growth repression. In the light, HAT1 inhibited BR biosynthesis but enhanced auxin signaling by directly repressing IAA3/SHORT HYPOCOTYL 2 (SHY2) expression. Our findings uncover a dual function of HAT1 in regulating BR biosynthesis and auxin signaling that is crucial for ensuring proper skotomorphogenic and photomorphogenic growth.

Performance of spherical and flat solar cells under different environmental conditions

This study investigates the performance degradation of spherical and flat solar modules due to dust accumulation. To isolate the effect of dust, both modules were subjected to identical environmental conditions, including irradiance, temperature, and humidity. The results reveal a significant reduction in power efficiency as dust density increases, with a nearly linear relationship observed. Spherical solar modules exhibited a minor decrease in efficiency (±3.64%) compared to flat solar modules (±5.46%) under increased dust density, consistent with prior research. Dust accumulation affects solar cells by causing dust deposition, light scattering, and temperature rise. Flat solar panels are more susceptible to dust accumulation due to their larger surface area, which attracts more dust particles. In contrast, spherical cells’ smaller surface area minimizes dust build-up, making them less prone to efficiency loss. The study also examined the impact of solar intensity and tilt angle on efficiency. Spherical cells demonstrated greater resilience in varying light conditions and tilt angles, maintaining efficiency across a wider range of scenarios than flat panels. These findings emphasize the importance of considering dust accumulation when designing and maintaining solar panels, with spherical modules offering advantages in dusty environments and under changing sunlight conditions.

Optimal Illumination Distance Metrics for Person Re-identification in Complex Lighting Conditions

Person Re-identification is extensively applied in public security and surveillance. However, environmental factors like time and location often lead to varying lighting conditions in captured pedestrian images, significantly impacting identification accuracy. Current approaches mitigate this issue through lighting transformation techniques, aiming to normalize images to a standard lighting condition for consistent person re-identification results. Yet, these methods overlook the fact that different content may hold distinct identification values under diverse lighting conditions. To address this, we conducted an analysis on the identification distance between images of the same or different pedestrians under predefined lighting conditions. From this analysis, we introduce the concept of optimal lighting: a condition where the distance between image pairs is minimized compared to other lighting scenarios. We propose utilizing this optimal lighting distance in the image retrieval process for final ranking. Our study, validated on synthetic datasets Market-IA and Duke-IA, demonstrates that optimal lighting is independent of image texture information. Each image pair exhibits a unique optimal lighting, yet consistently shows a minimum distance value.

PDC-YOLO: A Network for Pig Detection under Complex Conditions for Counting Purposes

Pigs play vital roles in the food supply, economic development, agricultural recycling, bioenergy, and social culture. Pork serves as a primary meat source and holds extensive applications in various dietary cultures, making pigs indispensable to human dietary structures. Manual pig counting, a crucial aspect of pig farming, suffers from high costs and time-consuming processes. In this paper, we propose the PDC-YOLO network to address these challenges, dedicated to detecting pigs in complex farming environments for counting purposes. Built upon YOLOv7, our model incorporates the SPD-Conv structure into the YOLOv7 backbone to enhance detection under varying lighting conditions and for small-scale pigs. Additionally, we replace the neck of YOLOv7 with AFPN to efficiently fuse features of different scales. Furthermore, the model utilizes rotated bounding boxes for improved accuracy. Achieving a mAP of 91.97%, precision of 95.11%, and recall of 89.94% on our collected pig dataset, our model outperforms others. Regarding technical performance, PDC-YOLO exhibits an error rate of 0.002 and surpasses manual counting significantly in speed.

Rapid monitoring of structural deformation based on unsupervised segmentation model

This paper presents a novel approach for rapid structural deformation monitoring using an enhanced segmentation model, SAM-DM (Segment Anything Model-Deformation Monitoring). The method enhances the SAM model by integrating an innovative point prompt generation technique, image fusion, and connected domain analysis, enabling structural deformation monitoring with just a single manual click on the target area in the initial video frame. Specifically, this manual click generates coordinate information for the target area, serving as a point prompt for the SAM-DM model. This process results in the creation of a high-quality binary mask, which is then fused with the original image to isolate only the target area. Subsequently, connected domain analysis is employed to automatically process the mask, extracting the pixel centroid coordinates of the target area, which are then used as prompts for segmenting subsequent frames. This approach allows continuous tracking of changes in the target area's coordinates, thereby facilitating rapid structural deformation monitoring. Experimental results demonstrate that this method can accurately monitor deformation across all target points and adapt to varying lighting conditions and noise levels. Applied to monitor the bending deformation of reinforced concrete beams and the in-plane deformation of concrete slabs, the method achieves results closely aligned with actual measurements, yielding relative errors of 0.95 % and 0.48 %, respectively. Additionally, in monitoring the vibrational deformation of steel frames, the primary frequency of the curve precisely matches measured values, confirming that the SAM-DM-based method delivers both high accuracy and strong robustness.

A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

Recent years have witnessed significant advancements in machine perception, particularly in the context of self-driving vehicles. The accurate detection and interpretation of road signs by these vehicles are crucial for enhancing safety, intelligence, and efficiency on the roads. Consequently, there is a growing body of research dedicated to improving traffic sign recognition technologies, a key component of intelligent transportation systems. Annual statistics highlight numerous accidents attributable to factors such as excessive speed, variable lighting conditions, and the misinterpretation of traffic signs. In response to these challenges, a novel approach for the rapid and reliable recognition of traffic signs by moving vehicles has been developed. This approach leverages a custom dataset encompassing twelve object categories and seven subcategories, reflective of road sign diversities encountered in India. A specialized algorithm, TrafficSignNet, was devised to specifically identify signs related to speed, turning, zones, and bumps. This algorithm was trained on a comprehensive dataset comprising 4,962 images, with its performance evaluated using 705 images from real traffic scenarios. The evaluation demonstrates that the model achieves remarkable accuracy across various lighting conditions, processing up to 12 frames per second. This processing rate is compatible with the high-definition standards of contemporary vehicle cameras, which is 1280 × 720 pixels. The model's effectiveness is quantified through accuracy, precision, recall, and F1 score, with respective values of 0.985, 0.978, 0.964, and 0.971, showcasing its potential to significantly contribute to the advancement of smart transportation systems.

Video Content Analysis for Detection of Road Traffic Congestion Pattern: An Optimized YOLOv8 Based Approach

This paper presents a new approach to Video Content Analysis (VCA) with a specific focus on detecting and analyzing road traffic congestion patterns. The implemented methodology deploys the YOLOv8 (You Only Look Once) object detection framework, which has been optimized to accurately and efficiently identify road traffic objects within video streams. The primary goal is to improve traditional VCA systems by capturing and understanding complex road congestion scenarios. This research develops a customized YOLO model optimized specifically for road traffic analysis. The optimization process addresses the challenges inherent in real-world traffic scenarios including variations in lighting conditions, diverse vehicle types and dynamic traffic flow patterns. The developed model seeks to enable robust and real-time detection of congestion-related events such as heavy congestion and normal congestion thereby contributing to improved traffic management and overall road safety. Moreover, the paper explores the integration of advanced techniques for congestion pattern analysis including vehicle detection, tracking, and counting. The combination of these features facilitates a comprehensive understanding of traffic dynamics and congestion evolution over time. The proposed approach is trained and evaluated on benchmark datasets and real-world video footage to assess its performance. Experimental results demonstrate the effectiveness of the optimized YOLOv8-based approach in accurately detecting and analyzing road traffic congestion patterns. The system exhibits promising capabilities in handling diverse and challenging scenarios thereby serving as a valuable tool for intelligent transportation systems, urban planning and traffic management. This research significantly contributes to the advancement of video-based traffic analysis, providing a robust solution for monitoring and mitigating congestion-related challenges on road networks.

Light-driven differences in bacterial networks and organic matter decomposition: Insights from an analysis of the harmful cyanobacterium Microcystis aeruginosa PCC 7806

Freshwater systems are critical yet often underestimated components of global carbon cycling, functioning both as carbon sinks and sources. Cyanobacteria play a key role in this cycle by capturing atmospheric carbon dioxide through photosynthesis. The captured carbon is either released back into the atmosphere or sequestered in sediments following organismal decay. This study examines the pivotal role of cyanobacteria, specifically Microcystis aeruginosa PCC 7806, in the biogeochemical cycling of carbon in freshwater ecosystems, with a focus on how light influences the degradation of cyanobacteria-derived organic matter. Using a combination of 16S rDNA sequencing and excitation-emission matrix coupled with parallel factor (EEM-PARAFAC) analysis, we conducted a 50-day experiment to investigate the dynamics of dissolved organic matter (DOM) and lysate organic matter (LOM) derived from M. aeruginosa PCC 7806 under light and dark conditions. Our results demonstrate that light significantly impacts bacterial community composition, gene functionality, and the decomposition of organic matter. The findings emphasize the crucial role of light in facilitating microbial adaptation, stabilizing microbial networks and driving organic substrate transformation. These insights underscore the influence of light on microbial community dynamics and organic matter degradation, revealing shifts in microbial populations under varying light conditions. This suggests a strong link between photochemical processes and microbial activity, with significant ecological implications.

Night time Surveillance in Communication Using YOLOv3

Nighttime surveillance presents significant challenges due to low visibility, varying lighting conditions, and interference from artificial light sources. Hence, this study proposed an effective object detection framework using the YOLOv3 model to enhance real-time monitoring in night surveillance applications, specifically within communication and security systems. YOLOv3's architecture, with its multi-scale detection and use of predefined anchor boxes, enables robust detection of objects under low-light environments and amidst light interference from vehicles and streetlights. The proposed system is tested on a night surveillance dataset, where it demonstrates high precision and speed in identifying objects, making it suitable for real-time applications. With Mean Average Precision (mAP) 87.9%, YOLOv3 effectively balances detection accuracy with inference time, ensuring minimal latency in live surveillance feeds. The results indicate that YOLOv3 outperforms traditional models such as Faster RCNN, particularly in detecting small objects under poor illumination. This approach offers a reliable solution for enhancing communication and security systems in nighttime surveillance scenarios.

Achieving the Best Symmetry by Finding the Optimal Clustering Filters for Specific Lighting Conditions

This article explores the efficiency of various clustering methods for image segmentation under different luminosity conditions. Image segmentation plays a crucial role in computer vision applications, and clustering algorithms are commonly used for this purpose. The search for an adaptive clustering mechanism aims to ensure the maximum symmetry of real objects with objects/segments in their digital representations. However, clustering method performances can fluctuate with varying lighting conditions during image capture. Therefore, we assess the performance of several clustering algorithms—including K-Means, K-Medoids, Fuzzy C-Means, Possibilistic C-Means, Gustafson–Kessel, Entropy-based Fuzzy, Ridler–Calvard, Kohonen Self-Organizing Maps and MeanShift—across images captured under different illumination conditions. Additionally, we develop an adaptive image segmentation system utilizing empirical data. Conducted experiments highlight varied performances among clustering methods under different luminosity conditions. This research enhances a better understanding of luminosity’s impact on image segmentation and aids the method selection for diverse lighting scenarios.

Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications.

Template matching is a common approach in bin-picking tasks. However, it often struggles in complex environments, such as those with different object poses, various background appearances, and varying lighting conditions, due to the limited feature representation of a single template. Additionally, during the bin-picking process, the template needs to be frequently updated to maintain detection performance, and finding an adaptive template from a vast dataset poses another challenge. To address these challenges, we propose a novel template searching method in a latent space trained by a Variational Auto-Encoder (VAE), which generates an adaptive template dynamically based on the current environment. The proposed method was evaluated experimentally under various conditions, and in all scenarios, it successfully completed the tasks, demonstrating its effectiveness and robustness for bin-picking applications. Furthermore, we integrated our proposed method with YOLO, and the experimental results indicate that our method effectively improves YOLO's detection performance.

Tunability in electronic and optical properties of GaS/PbS vdW heterostructure

A promising novel class of heterostructures has recently emerged, combining a semiconducting GaS monolayer with other 2D materials for energy-related applications. In this study, we considered the layered PbS to form the van der Waals heterostructure with GaS and investigated its properties using density functional theory. The GaS/PbS heterostructure exhibits a type-II heterostructure with an indirect bandgap of 1.65 eV, displaying enhanced light absorption across the visible spectrum. Moreover, the heterostructure's energy band gap shows tunability with an applied transverse electric field attributed to the spontaneous electric polarization within the lattice. Subsequently, it contributes to increased optical absorbance and light harvesting efficiency under ±0.2 V/Å electric field. The applied electric field also offers tunable band alignments (transition type-II and type-I), making it a potential candidate for solar cells that can optimize their efficiency based on varying light conditions.