Search Results (42)

The COVID-19 pandemic exposed critical deficiencies in the United States’ legal system’s handling of emergency injunctions, particularly concerning religious freedom. This article examines the challenges courts faced in balancing public health measures with constitutional rights, focusing on the use of shadow dockets and the frequent dismissal of cases due to mootness. Analyzing key Supreme Court decisions and lower court rulings, we highlight the inconsistencies and delays that arose when addressing First Amendment challenges to pandemic-related restrictions. Arguments for procedural reforms, including expedited hearings and avoiding mootness dismissals in cases of national importance, are provided to protect fundamental rights during future public health crises. Full article

Urban regulations in Amman, Jordan, enforce uniform building setbacks irrespective of topography, exacerbating shading effects and compromising daylight access in residential areas—a critical factor for occupant health and psychological well-being. This study evaluates the interplay between standardized setbacks, slope variations (0–30%), and shadow patterns in Amman’s dense, mountainous urban fabric. Focusing on the Al Jubayhah district, a mixed-methods approach was used, combining field surveys, 3D modeling (Revit), and seasonal shadow simulations (March, September, December) to quantify daylight deprivation. The results reveal severe shading in winter (78.3% site coverage in December) and identify slope-dependent setbacks as a key determinant: for instance, a 15 m building on a 30% slope requires a 26.4 m rear setback to mitigate shadows, compared to 13.8 m on flat terrain. Over 39% of basements in the study area remain permanently shaded due to retaining walls, correlating with poor living conditions. The findings challenge Amman’s one-size-fits-all regulatory framework (Building Code No. 67, 1979), and we propose adaptive guidelines, including slope-adjusted setbacks, restricted basement usage, and optimized street orientation. This research underscores the urgency of context-sensitive urban policies in mountainous cities to balance developmental density with daylight equity, offering a replicable methodology for similar Mediterranean climates. Full article

Unmanned Surface Vehicles (USVs) are commonly used as mobile docking stations for Unmanned Aerial Vehicles (UAVs) to ensure sustained operational capabilities. Conventional vision-based techniques based on horizontally-placed fiducial markers for autonomous landing are not only susceptible to interference from lighting and shadows but are also restricted by the limited Field of View (FOV) of the visual system. This study proposes a method that integrates an improved minimum snap trajectory planning algorithm with an event-triggered vision-based technique to achieve autonomous landing on a small USV. The trajectory planning algorithm ensures trajectory smoothness and controls deviations from the target flight path, enabling the UAV to approach the USV despite the visual system’s limited FOV. To avoid direct contact between the UAV and the fiducial marker while mitigating the interference from lighting and shadows on the marker, a landing platform with a vertically placed fiducial marker is designed to separate the UAV landing area from the fiducial marker detection region. Additionally, an event-triggered mechanism is used to limit excessive yaw angle adjustment of the UAV to improve its autonomous landing efficiency and stability. Experiments conducted in both terrestrial and river environments demonstrate that the UAV can successfully perform autonomous landing on a small USV in both stationary and moving scenarios. Full article

Small and medium-sized businesses (SMEs) are an essential subject of economic activity in any country because, without their participation, the development and formation of the very structure of the economy are almost impossible. The role of SMEs is significant since these businesses allow for an increase in the number of jobs, develop competition, and, as a result, improve the quality of goods, creating different price segments. More than 4 million people are employed in this sector in the Republic of Kazakhstan, and their share of GDP is 36.7%. The accelerated contribution of the SME sector to Kazakhstan’s GDP has led to the need to conduct a study in this area. This study analyzes the impact of SME production output on Kazakhstan’s economic growth by considering some macroeconomic variables using the ARDL model. The study’s findings confirm that SME output positively and significantly impacts economic growth. The government of Kazakhstan has been implementing a series of policies and incentive programs to increase the contribution of the SME sector to economic growth since the years of independence. However, SMEs are not able to reach their full potential due to various restrictions that limit their expansion. This study offers some suggestions for the development of the SME sector. In order to ensure SME concentration in the economy, investment in R&D should be a priority incentive. On the other hand, we should recognize the shadow economy problem in the country. Full article

Lane detection is a pivotal technology of the intelligent driving system. By identifying the position and shape of the lane, the vehicle can stay in the correct lane and avoid accidents. Image-based deep learning is currently the most advanced method for lane detection. Models using this method already have a very good recognition ability in general daytime scenes, and can almost achieve real-time detection. However, these models often fail to accurately identify lanes in challenging scenarios such as night, dazzle, or shadows. Furthermore, the lack of diversity in the training data restricts the capacity of the models to handle different environments. This paper proposes a novel method to train CycleGAN with existing daytime and nighttime datasets. This method can extract features of different styles and multi-scales, thereby increasing the richness of model input. We use CycleGAN as a domain adaptation model combined with an image segmentation model to boost the model’s performance in different styles of scenes. The proposed consistent loss function is employed to mitigate performance disparities of the model in different scenarios. Experimental results indicate that our method enhances the detection performance of original lane detection models in challenging scenarios. This research helps improve the dependability and robustness of intelligent driving systems, ultimately making roads safer and enhancing the driving experience. Full article

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website. Full article

Knowledge-driven building extraction method exhibits a restricted adaptability scope and is vulnerable to external factors that affect its extraction accuracy. On the other hand, data-driven building extraction method lacks interpretability, heavily relies on extensive training data, and may result in extraction outcomes with building boundary blur issues. The integration of pre-existing knowledge with data-driven learning is essential for the intelligent identification and extraction of buildings from high-resolution aerial images. To overcome the limitations of current deep learning building extraction networks in effectively leveraging prior knowledge of aerial images, a geometric significance-aware deep mutual learning network (GSDMLNet) is proposed. Firstly, the GeoSay algorithm is utilized to derive building geometric significance feature maps as prior knowledge and integrate them into the deep learning network to enhance the targeted extraction of building features. Secondly, a bi-directional guidance attention module (BGAM) is developed to facilitate deep mutual learning between the building feature map and the building geometric significance feature map within the dual-branch network. Furthermore, the deployment of an enhanced flow alignment module (FAM++) is utilized to produce high-resolution, robust semantic feature maps with strong interpretability. Ultimately, a multi-objective loss function is crafted to refine the network’s performance. Experimental results demonstrate that the GSDMLNet excels in building extraction tasks within densely populated and diverse urban areas, reducing misidentification of shadow-obscured regions and color-similar terrains lacking building structural features. This approach effectively ensures the precise acquisition of urban building information in aerial images. Full article

Background: T1D is a severe metabolic disorder due to selective autoimmune pancreatic islet β-cell killing, which results in complete abrogation of endogenous insulin secretion. The affected patients, once the disease is clinically overt, must immediately undertake insulin supplementation according to intensive therapy regimens to prevent the onset of acute and chronic complications, some of them potentially lethal. Replacement of the destroyed β-cells with fresh and vital pancreatic endocrine tissue, either of the whole organ or isolated islets transplantation, started a few decades ago with progressively encouraging results, although exogenous insulin withdrawal was obtained in a minor cohort of the treated patients. The restricted availability of donor organs coupled with general immunosuppression treatment of recipients to avoid graft immune rejection may, at least partially, explain the limited success achieved by these procedures. Results: The introduction of pluripotent stem cells (either of human embryonic origin or adult cells genetically induced to pluripotency) that can be differentiated toward insulin secretory β-like cells could provide an indefinite resource for insulin-producing cells (IPCs). Conclusions: Because the use of human embryos may encounter ethical problems, employment of adult multipotent mesenchymal stem cells (MSCs) extracted from several tissues may represent an alternative option. MSCs are associated with strong immunoregulatory properties that can alter early stages of β-cell-directed autoimmunity in T1D, other than holding the potential to differentiate themselves into β-like cells. Lights and shadows of these new strategies for the potential cure of T1D and their advancement state are reviewed. Full article

In this work, we test the ability of an exact solution, found in the framework of a nonlinear extension of the Israel–Stewart theory, to fit the supernovae Ia, gravitational lensing, and black hole shadow data. This exact solution is a generalization of one previously found for a dissipative unified dark matter model in the context of the near-equilibrium description of dissipative processes, where we do not have the full regime of the nonlinear picture. This generalized solution is restricted to the case where a positive entropy production is guaranteed and is tested under the condition that ensures its causality, local existence, and uniqueness. From the observational constraints, we found that this generalized solution is a good candidate in the description of the observational late-time data used in this work, with best-fit values of $H_0={73.2}_{-0.9}^{+0.8}{\displaystyle \frac{km/s}{Mpc}}$, $q_0=-{0.41}_{-0.03}^{+0.03}$, ${\widehat{\xi }}_0={0.88}_{-0.17}^{+0.09}$, $ϵ={0.34}_{-0.04}^{+0.03}$, and $k={0.27}_{-0.20}^{+0.37}$, at a $1\sigma (68.3\%)$ of confidence level. We show that the nonlinear regime of the Israel–Stewart theory consistently describes the recent accelerated expansion of the universe without the inclusion of some kind of dark energy component and also provides a more realistic description of the fluids that make up the late universe. Full article

As one of the world’s largest economies and the biggest emitter of greenhouse gases, China plays a critical role in global environmental management. As China emphasizes new quality productive forces, understanding how green finance can enable green innovation quality (GIQ) is essential for projecting China’s influence in the sustainable development of the global ecological environment. This paper sets up a quasi-natural experiment using the Green Credit Policy (GCP) to examine the impact of green financial credit allocation on the enterprises’ GIQ. The findings demonstrate that the GCP has the potential to improve the GIQ of the green credit-restricted industries, compared to non-green credit-restricted ones. It is worth noting that as China speeds up its industrial digital transformation and productivity improvement, green financial credit allocation can elevate the digitization level and total factor productivity of green credit-restricted industries, leading to a higher GIQ by curbing corporate shadow banking. Further research shows that fintech and financial regulation can strengthen the positive influence of the GCP on GIQ. Moreover, regional intellectual property protection has a beneficial synergistic effect in combination with the policy. Full article

The detection of burnt areas from satellite imagery is one of the most straightforward and useful applications of satellite remote sensing. In general, the approach relies on a change detection analysis applied on pre- and post-event images. This change detection analysis usually is carried out by comparing the values of specific spectral indices such as: NBR (normalised burn ratio), BAI (burn area index), MIRBI (mid-infrared burn index). However, some potential sources of error arise, particularly when near-real-time automated approaches are adopted. An automated approach is mandatory when the burnt area monitoring should operate systematically on a given area of large size (country). Potential sources of errors include but are not limited to clouds on the pre- or post-event images, clouds or topographic shadows, agricultural practices, image pixel size, level of damage, etc. Some authors have already noted differences between global databases of burnt areas based on satellite images. Sources of errors could be related to the spatial resolution of the images used, the land-cover mask adopted to avoid false alarms, and the quality of the cloud and shadow masks. This paper aims to compare different burnt areas datasets (EFFIS, ESACCI, Copernicus, FIRMS, etc.) with the objective to analyse their differences. The comparison is restricted to the Italian territory. Furthermore, the paper aims to identify the degree of approximation of these satellite-based datasets by relying on ground survey data as ground truth. To do so, ground survey data provided by CUFA (Comando Unità Forestali, Ambientali e Agroalimentari Carabinieri) and CFVA (Corpo Forestale e Vigilanza Ambientale Sardegna) were used. The results confirm the existence of significant differences between the datasets. The subsequent comparison with the ground surveys, which was conducted while also taking into account their own approximations, allowed us to identify the accuracy of the satellite-based datasets. Full article

The present work is amongst the few that attempt to critically assess the maturity of Business Model (BM) and strategy disclosures of listed firms under the shadow of the new EU reporting directive, the Corporate Sustainability Reporting Directive (CSRD). The novel Practices Evaluation Approach (PEA), developed recently by the Project Task Force on Reporting of Non-Financial Risks and Opportunities (PTF-RNFRO), offers the evaluation framework for this assessment. The PEA delineates and evaluates the maturity of BM and strategy disclosures against qualitative characteristics and content elements drawn from well-accepted, financial and non-financial, reporting frameworks, standards and directives (including the CSRD). Therefore, the PEA provides the advantage of a contemporary and integrated/holistic assessment tool. Specifically, the following seven evaluation criteria are used for the assessment: clarity and comprehensiveness of the overall BM, strategy disclosure, disclosure of the BM’s potential across-time horizons and its dependencies, impacts on sustainability issues, material sustainability issues that are likely to affect the company’s performance, the BM’s exposure to sustainability risks and sustainability opportunities, and sustainability strategy, targets, KPIs and their monitoring and progress. The analysis covered 30 CSR/sustainability reports and connected documents of listed companies operating in 6 key sectors of the Greek economy, i.e., information technology, construction, tourism and transportation, cosmetics, banking and energy. The results of our analysis offer evidence that BM reporting is not holistically developed (i.e., critical components are missing), and the level of development varies across the examined sectors. Moreover, sustainability risks are more stressed, in relevance to opportunities, whilst positive (rather than negative) impacts are mainly disclosed. Also, the quantification of sustainability risks and opportunities does not appear frequently, whilst the interconnections between sustainability strategy and companies’ financial objectives is relatively restricted. The paper concludes by pointing out some critical hints useful for enhancing the maturity of BM and strategy disclosures. Full article

Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB promoted. However, roads in the real world are generally exposed to intricate noise conditions as a result of changing weather and climate effects; these include sunshine spots, shadowing caused by trees or physical facilities, traffic obstacles and signs, and cracks or sealing signs resulting from long-term road usage, as well as different types of road materials, such as cement or asphalt; all of these factors greatly influence the effectiveness of road segmentation. In this work, we investigate the extension of Primordial U-Net by the proposed EnRDeA U-Net, which uses an input channel applying a Residual U-Net block as an encoder and an attention gate in the output channel as a decoder, to validate a dataset of intricate road noises. In addition, we carry out a detailed analysis of the nets’ features and segmentation performance to validate the intricate noises dataset on three U-Net extensions, i.e., the Primordial U-Net, Residual U-Net, and EnRDeA U-Net. Finally, the nets’ structures, parameters, training losses, performance indexes, etc., are presented and discussed in the experimental results. Full article

With the increasing popularity of IoT (Internet-of-Things) devices, their security becomes an increasingly important issue. Buffer overflow vulnerabilities have been known for decades, but are still relevant, especially for embedded devices where certain security measures cannot be implemented due to hardware restrictions or simply due to their impact on performance. Therefore, many buffer overflow detection mechanisms check for overflows only before critical data are used. All data that an attacker could use for his own purposes can be considered critical. It is, therefore, essential that all critical data are checked between writing a buffer and its usage. This paper presents a vulnerability of the ESP32 microcontroller, used in millions of IoT devices, that is based on a pointer that is not protected by classic buffer overflow detection mechanisms such as Stack Canaries or Shadow Stacks. This paper discusses the implications of vulnerability and presents mitigation techniques, including a patch, that fixes the vulnerability. The overhead of the patch is evaluated using simulation as well as an ESP32-WROVER-E development board. We showed that, in the simulation with 32 general-purpose registers, the overhead for the CoreMark benchmark ranges between 0.1% and 0.4%. On the ESP32, which uses an Xtensa LX6 core with 64 general-purpose registers, the overhead went down to below 0.01%. A worst-case scenario, modeled by a synthetic benchmark, showed overheads up to 9.68%. Full article

In close-range photogrammetry, circular-coded targets (CCTs) are a reliable method to solve the issue of image correspondence. Currently, the identification methods for CCTs are very mature, but complex illumination conditions are still a key factor restricting identification. This article proposes an adaptive homomorphic filtering (AHF) algorithm to solve this issue, utilizing homomorphic filtering (HF) to eliminate the influence of uneven illumination. However, HF parameters vary with different lighting types. We use a genetic algorithm (GA) to carry out global optimization and take the identification result as the objective function to realize automatic parameter adjustment. This is different from the optimization strategy of traditional adaptive image enhancement methods, so the most significant advantage of the proposed algorithm lies in its automation and universality, i.e., users only need to input photos without considering the type of lighting conditions. As a preprocessing algorithm, we conducted experiments combining advanced commercial photogrammetric software and traditional identification methods, respectively. We cast stripe- and lattice-structured light to create complex lighting conditions, including uneven lighting, dense shadow areas, and elliptical light spots. Experiments showed that our algorithm significantly improves the robustness and accuracy of CCT identification methods under complex lighting conditions. Given the perfect performance under stripe-structured light, this algorithm can provide a new idea for the fusion of close-range photogrammetry and structured light. This algorithm helps to improve the quality and accuracy of photogrammetry and even helps to improve the decision making and planning process of photogrammetry. Full article