Search Results (18)

Urban and peri-urban regions increasingly face climate-related pressures, competing land-use demands, and the need to expand renewable-energy infrastructure while maintaining agricultural land and landscape functions. Agrivoltaics, which combines photovoltaic energy generation with agricultural production, represents a potentially multifunctional approach to land use; however, regional GIS assessments often stop at environmental suitability surfaces without translating those results into planning-relevant cadastral inventories. This study develops and applies a planning-oriented Geographic Information System (GIS) framework for preliminary statewide agrivoltaic screening in Connecticut. Annual global solar radiation and terrain slope were integrated through a weighted suitability model, while incompatible land-cover classes were treated as hard exclusions through a binary land-cover mask. The workflow subsequently excluded protected and open-space lands, associated suitable areas with cadastral parcels, normalized and dissolved parcel identifiers using ParcelKey, and a recalculated suitable area from the resulting unique parcel geometries and then applied a minimum requirement of 1 ha of cumulative suitable area per retained parcel. The final baseline inventory contained 3497 normalized unique cadastral parcels encompassing 16,366.49 ha of GIS-identified suitable area, with suitable land representing an average of 42.46% of total parcel area. Peri-urban contexts accounted for the largest share of the final suitable area, containing 2497 parcels and 73.16% of the total, compared with 476 urban and 524 rural parcels. Sensitivity analysis indicated strong stability under alternative weighting schemes, with spatial overlap exceeding 99% relative to the baseline. Reducing the suitability-score threshold from 3.0 to 2.5 produced only minor changes, whereas increasing it to 3.5 reduced the inventory to 3095 parcels and 13,712.89 ha. From a sustainability perspective, the framework provides a spatial decision-support approach for coordinating renewable-energy planning with agricultural land stewardship, conservation constraints, and more efficient use of already fragmented land resources. By making the effects of exclusions, parcel thresholds, and analytical assumptions explicit, the approach supports more transparent and reproducible evaluation of land-use trade-offs relevant to sustainable development. The resulting inventory is intended as a first-stage planning resource rather than a determination of project feasibility or site-level sustainability performance. Full article

Multimodal biometric systems have become essential components of modern electronic identity and authentication platforms where robustness under real-world degradation is critical. However, opaque face masks impose severe facial occlusion and attenuate high-frequency spectral components. Conversely, transparent face shields introduce complex specular reflections and act as an acoustic channel distortion source. Addressing these asymmetric degradation challenges, this paper proposes a reliability-aware Dynamic Score Fusion (DSF) for multimodal biometric identification. The proposed method performs sample-level reliability estimation for both face and voice modalities at the input stage. This enables sample-wise adaptive weighting of modality scores based on their estimated reliability. The framework integrates an ElasticFace-Arc backbone for face recognition with an Emphasized Channel Attention, Propagation and Aggregation—Time Delay Neural Network (ECAPA-TDNN) for speaker identification. The proposed approach is evaluated on the FaciaVox dataset, comprising face images and voice recordings acquired under multiple face-covering conditions. Experiments under the Standard to Cross-Condition Protocol (SCCP) and Multi-Condition Protocol (MCP) demonstrate that the proposed DSF consistently outperforms conventional score-level fusion methods, including Weighted Sum Fusion (WSF) and Logistic Regression Fusion (LRF). It achieves average Rank-1 accuracies of 89.6% (SCCP) and 93.7% (MCP), with gains of up to 9.3 percentage points over these baselines. The reliability estimators further demonstrate strong predictive capability, yielding Area Under the Curve (AUC) values above 0.95 for both modalities in distinguishing correctly and incorrectly identified samples under the closed-set identification setting. These findings confirm that sample-wise reliability modeling provides an effective mechanism for enhancing multimodal biometric performance under challenging mask and shield conditions, supporting the deployment of robust AI-driven electronic identification systems. Full article

Detecting minute defects, such as spurs on the surface of a Printed Circuit Board (PCB), is extremely challenging due to their small size (average size < 20 pixels), sparse features, and high dependence on circuit topology context. The original Mosaic data augmentation method faces significant challenges with semantic adaptability when dealing with such tasks. Its unrestricted random cropping mechanism easily disrupts the topological structure of minute defects attached to the circuits, leading to the loss of key features. Moreover, a splicing strategy without domain constraints struggles to simulate real texture interference in industrial settings, making it difficult for the model to adapt to the complex and variable industrial inspection environment. To address these issues, this paper proposes a Context-aware and Domain-consistent Mosaic (CD-Mosaic) augmentation algorithm. This algorithm abandons pure randomness and constructs an adaptive augmentation framework that synergizes feature fidelity, geometric generalization, and texture perturbation. Geometrically, an intelligent sampling and dynamic integrity verification mechanism, driven by “utilization-centrality”, is designed to establish a controlled sample quality distribution. This prioritizes the preservation of the topological semantics of dominant samples to guide feature convergence. Meanwhile, an appropriate number of edge-truncated samples are strategically retained as geometric hard examples to enhance the model’s robustness against local occlusion. For texture, a dual-granularity visual perturbation strategy is proposed. Using a homologous texture library, a hard mask is generated in the background area to simulate foreign object interference, and a local transparency soft mask is applied in the defect area to simulate low signal-to-noise ratio imaging. This strategy synthesizes visual hard examples while maintaining photometric consistency. Experiments on an industrial-grade PCB dataset containing 2331 images demonstrate that the YOLOv11m model equipped with CD-Mosaic achieves a significant performance improvement. Compared with the native Mosaic baseline, the core metrics mAP@0.5 and Recall reach 0.923 and 86.1%, respectively, with a net increase of 8.3% and 8.8%; mAP@0.5:0.95 and AP_small, which characterize high-precision localization and small target detection capabilities, are improved to 0.529 (+3.0%) and 0.534 (+3.3%), respectively; the comprehensive metric F1-score jumps to 0.903 (+6.2%). The experiments prove that this method effectively solves the problem of missed detections of industrial minute defects by balancing sample quality and detection difficulty. Moreover, the inference speed of 84.9 FPS fully meets the requirements of industrial real-time detection. Full article

Background: Face masks are used in healthcare for the prevention of the spread of disease; however, the recent COVID-19 pandemic raised awareness of the challenges of typical opaque masks that obscure nonverbal cues. In addition, various masks have been shown to attenuate speech above 1000 Hz, and lack of nonverbal cues exacerbates speech understanding in the presence of background noise. Transparent masks can help to overcome the loss of nonverbal cues, but they have greater attenuative effects on higher speech frequencies. This study evaluated a newer prototype transparent face mask redesigned from a version evaluated in a previous study. Methods: Thirty participants (10 with normal hearing, 10 with moderate hearing loss, and 10 with severe-to-profound hearing loss) were recruited. Selected lists from the Connected Speech Test (CST) were digitally recorded using male and female talkers and presented to listeners at 65 dB HL in 12 conditions against a background of 4-talker babble (+5 dB SNR): without a mask (auditory only and audiovisual), with an opaque mask (auditory only and audiovisual), and with a transparent mask (auditory only and audiovisual). Results: Listeners with normal hearing performed consistently well across all conditions. For listeners with hearing loss, speech was generally easier to understand with the male talker. Audiovisual conditions were better than auditory-only conditions, and No Mask and Transparent Mask conditions were better than Opaque Mask conditions. Conclusions: These findings continue to support the use of transparent masks to improve communication, minimize medical errors, and increase patient satisfaction. Full article

With the widespread adoption of face masks due to global health crises and heightened security concerns, traditional face recognition systems have struggled to maintain accuracy, prompting significant research into masked face recognition (MFR). Although various models have been proposed, a comprehensive and systematic understanding of recent deep learning (DL)-based approaches remains limited. This paper addresses this research gap by providing an extensive review and comparative analysis of state-of-the-art MFR techniques. We focus on DL-based methods due to their superior performance in real-world scenarios, discussing key architectures, feature extraction strategies, datasets, and evaluation metrics. This paper also introduces a structured methodology for selecting and reviewing relevant works, ensuring transparency and reproducibility. As a contribution, we present a detailed taxonomy of MFR approaches, highlight current challenges, and suggest potential future research directions. This survey serves as a valuable resource for researchers and practitioners seeking to advance the field of robust facial recognition in masked conditions. Full article

By 2050, 700 million people will have hearing loss, requiring rehabilitation services. For about 80% of deaf and hard-hearing individuals, face coverings hinders their ability to lip-read. Also, the normal hearing population experiences issues socializing when wearing face masks. Therefore, there is a need to evaluate and further develop transparent face masks. In this work, the properties of polymers used in ten commercial transparent face masks were determined. The chemical composition of the polymers including nose bridges and ear loops was determined by FTIR spectroscopy. The focus of the characterizations was on the polymers in the transparent portion of each face mask. In half of the masks, the transparent portion contained PET, while in the other masks it consisted of PETG, PC, iPP, PVC, or SR (silicone rubber). Most masks had been coated with anti-fog material, and a few with scratch-resistant compounds, as indicated by XRF/EDX, SEM/EDX, and contact angle measurements. Thermal, molecular weight, and mechanical properties were determined by TGA/DSC, SEC, and tensile tests, respectively. To measure optical properties, UV-Vis reflectance and UV-Vis haze were applied. An assessment of the ten masks and recommendations to develop better transparent face masks were made, including improvement of their sustainability. Full article

As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. Recently, a novel counterfactual explanation model, CELS, has been introduced. CELS learns a saliency map for the interests of an instance and generates a counterfactual explanation guided by the learned saliency map. While CELS represents the first attempt to exploit learned saliency maps not only to provide intuitive explanations for the reason behind the decision made by the time series classifier but also to explore post hoc counterfactual explanations, it exhibits limitations in terms of its high validity for the sake of ensuring high proximity and sparsity. In this paper, we present an enhanced approach that builds upon CELS. While the original model achieved promising results in terms of sparsity and proximity, it faced limitations in terms of validity. Our proposed method addresses this limitation by removing mask normalization to provide more informative and valid counterfactual explanations. Through extensive experimentation on datasets from various domains, we demonstrate that our approach outperforms the CELS model, achieving higher validity and producing more informative explanations. Full article

This study investigates the public relations (PR) challenges faced by the airline industry during the COVID-19 pandemic, with Spirit Airlines as a focal case. Using a mixed-methods approach, this study analyzes a dataset of 344 LinkedIn online reviews and digital reactions to an incident where a family was removed from a Spirit Airlines flight after their two-year-old child refused to wear a mask. The case study highlights the complex PR challenges airlines face in balancing public health protocols with customer relations during health crises. Through thematic and sentiment analyses, this research identifies gaps in traditional crisis communication models, advocating for empathetic, transparent strategies that align with pandemic-related sensitivities. It underscores the need for specialized staff training to effectively manage such crises. The findings suggest that conventional PR strategies fall short in addressing the multifaceted nature of pandemic-induced crises, calling for a shift towards human-centered communication and robust stakeholder management. This study contributes to the discourse on crisis communication in the airline industry, offering actionable insights for balancing public health responsibilities with customer satisfaction. It calls for a reevaluation of established crisis communication frameworks, urging future research to explore more inclusive and adaptive PR practices in response to health emergencies. Full article

Since the introduction of polydimethylsiloxane (PDMS) microfluidic devices at the beginning of the 21st century, this elastomeric polymer has gained significant attention in the engineering community due to its biocompatibility, exceptional mechanical and optical properties, thermal stability, and versatility. PDMS has been widely used for in vitro experiments ranging from the macro- to nanoscale, enabling advances in blood flow studies, biomodels improvement, and numerical validations. PDMS devices, including microfluidic systems, have been employed to investigate different kinds of fluids and flow phenomena such as in vitro blood flow, blood analogues, the deformation of individual cells and the cell free layer (CFL). The most recent applications of PDMS involve complex hemodynamic studies such as flow in aneurysms and in organ-on-a-chip (OoC) platforms. Furthermore, the distinctive properties of PDMS, including optical transparency, thermal stability, and versality have inspired innovative applications beyond biomedical applications, such as the development of transparent, virus-protective face masks, including those for SARS-CoV-2 and serpentine heat exchangers to enhance heat transfer and energy efficiency in different kinds of thermal systems. This review provides a comprehensive overview of the current research performed with PDMS and outlines some future directions, in particular applications of PDMS in engineering, including biomicrofluidics, in vitro biomodels, heat transfer, and face masks. Additionally, challenges related to PDMS hydrophobicity, molecule absorption, and long-term stability are discussed alongside the solutions proposed in the most recent research studies. Full article

Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. Methods: Tree-based models (TBMs) such as XGBoost, LightGBM, CatBoost, Random Forests (RFs), Voting Classifier ensembles, and sequential models (LSTM, GRU) were trained on the MIMIC-III and IV datasets. A preprocessing pipeline addressed missing data, class imbalances, and synthetic data flagged with masks. Models were evaluated using a 5-min prediction window with minute-level interpolations for timely interventions. Results: TBMs outperformed sequential models in speed, interpretability, and reliability, making them better suited for real-time decision-making. Feature importance analysis identified six key physiological variables from the enhanced NEWS2+ score and emphasized the value of mask and score features for transparency. Voting Classifier ensembles showed slight metric gains but did not outperform individually optimized models, facing a precision-sensitivity tradeoff and slightly lower F1-scores for key severity levels. Conclusions: TBMs were effective for real-time hypoxemia prediction, while sequential models, though better at temporal handling, were computationally costly. This study highlights ML’s potential to improve triage systems and reduce alarm fatigue, with future plans to incorporate multi-hospital datasets for broader applicability. Full article

Previous research has found that, when firms engage in environmental sustainability practices, they tend to give a consistent signal to external stakeholders by acting in a more responsible, transparent, and ethical manner, and these firms tend to exhibit high earnings quality. However, other studies have found that those activities may mask a poor earnings quality. On the other hand, firms with high debt levels face constraints in raising funds. In this study, we expect these firms, when involved in environmental reporting practices, to reveal an increase in their earnings quality in order to improve their ability to capture financing. Thus, we analyze whether the level of environmental disclosure and a firm’s debt increase earnings quality. To analyze the former association, we develop an environmental sustainability reporting index (ESReporting), based on GRI standards, using the content analysis for Portuguese firms from 2016 to 2020. We use earnings persistence as a proxy for earnings quality because it is a fundamental characteristic to determine firm value. Regarding debt, we include a financial indicator to analyze its effect on earnings persistence. To test the hypotheses, we estimate a multiple linear regression, applying panel data. Our results suggest that ESReporting and debt tend to positively affect earnings persistence. In addition, our evidence suggests that ESReporting produces a higher positive impact then debt. These results show that ESReporting and debt may be used as regulating mechanisms of earnings management. Besides, this article brings some insights to the improvement of earnings quality resulting from a higher commitment to environmental disclosure and contributing to monitoring managers’ activities. Full article

Background: This study aimed to explore the effect of knowledge, COVID-19-related perceptions, and public trust on protective behaviors in Turkish people. Methods: Data were collected from an online survey (Turkish COVID-19 Snapshot Monitoring) conducted between July 2020 and January 2021. The recommended protective behaviors (hand cleaning, wearing a face mask, and physical distancing) to prevent COVID-19 were examined. The impacts of the following variables on protective behaviors were investigated using logistic regression analysis: knowledge, cognitive and affective risk perception, pandemic-related worry content, public trust, conspiracy thinking, and COVID-19 vaccine willingness. Results: Out of a total of 4210 adult respondents, 13.8% reported nonadherence to protection behavior, and 86.2% reported full adherence. Males and young (aged 18–30 years) people tend to show less adherence. Perceived self-efficacy, susceptibility, and correct knowledge were positively related to more adherence to protective behavior. Perceptual and emotional factors explaining protective behavior were perceived proximity, stress level, and worrying about the relatives who depended on them. Trust in health professionals and vaccine willingness were positive predictors, while conspiracy thinking and acquiring less information (<2, daily) were negative predictors. Unexpectedly, trust in the Ministry of Health showed a weak but negative association with protection behavior. Conclusions: Perceived stress, altruistic worries, and public trust seem to shape protection behaviors in addition to individuals’ knowledge and cognitive risk perception in respondents. Males and young people may have a greater risk for nonadherence. Reliable, transparent, and culture-specific health communication that considers these issues is required. Full article

This paper examines the COVID-19 pandemic response in Iran and offers speculations on the possible impact of its experience on the future response to other health emergencies and disaster risk management based on the lessons learned. The COVID-19 experience in Iran is unique in several aspects, including the significant role played by the healthcare workers’ sharing and exchange of information through Internet-based networking applications, and a sociocultural environment that was weakening public trust and cooperation in the use of preventive strategies such as less than the optimum wearing of face masks and attending large social gatherings. There was also hesitation in receiving the necessary vaccine doses due to public skepticism over the effectiveness of domestic COVID-19 vaccines. Furthermore, healthcare workers and health services were afflicted with a lack of sufficient manpower and material resources to fight the pandemic. Moreover, a strong and mostly negative influence of political agenda and religious influence on preventive health policies, especially an initial governmental ban on the import and use of Western vaccines and the pressure to hold religious festivals during the outbreaks, were prevalent. The lessons that can be learned from this ongoing crisis include the value of independent healthcare information networks, transparency in the communication of health information to the public to get their trust and cooperation, and an emphasis on the separation of health policies from political and religious interference. Full article

Background. The COVID-19 pandemic has imposed radical behavioral and social changes in the general population, significantly impacting the lives of individuals affected by disabilities. The aim of this study was to investigate the impact of COVID-19 on non-institutionalized subjects with sensorineural disabilities during the first COVID-19 wave in Italy. Methods. A 39-item online national survey was disseminated from 1 April 2020 to 31 June 2020 via social media throughout Italy to communities of individuals with proven severe sensorineural disabilities, affiliated to five national patient associations. The survey collected extensive information on the socio-demographic profile, health, everyday activities, and lifestyle of individuals with hearing and visual disabilities. Results. One hundred and sixty-three respondents with hearing (66.9%) and visual (33.1%) disabilities returned a usable questionnaire. The mean age of interviewees was 38.4 ± 20.2 years and 56.3% of them were females. Despite the vast majority of respondents (77.9%) perceiving their health status as unchanged (68.8% of interviewees with hearing deficits vs. 96.3% of those with visual impairments), about half the interviewees reported sleep disorders during lock-down, more likely those with visual deficits. Remote services were seemingly more effective for business than school activities. Furthermore, although just 18.8% of respondents rated remote rehabilitation care unsatisfactory, only 12.8% of interviewees felt supported by health and social services during the COVID-19 emergency. The vast majority of respondents were concerned about the future and the risk of SARS-CoV-2 contagion, particularly individuals with hearing impairments. Among the various risk mitigation measures, facemasks caused the greatest discomfort due to communication barriers, particularly among interviewees affected by hearing disabilities (92.2% vs. 45.7%). The most common request (46.5%) of respondents to reduce the inconveniences of the COVID-19 emergency was improving the access to and delivery of health and social services for individuals with sensorineural disabilities (19.3%), followed by the use of transparent masks (17.5%). Conclusions. Although health protection measures such as face masks and social distancing play a key role in preventing and controlling the spread of SARS-CoV-2, the unmet needs of disabled individuals should be carefully considered, especially those affected by sensory disabilities. Tailored access to health and social services for individuals affected by sensorineural disabilities should be implemented. Additional actions should include the use of face shields as a valid alternative to face masks to reduce communication barriers linked to hearing-impairment, as well as the improvement of remote services, especially distance learning at school. Full article

Here we present a device that suppresses transfer of aerosol between nearby seating areas through the use of optically transparent, sound transmitting barriers and HEPA fan filter unit (FFU). A potential application of this device is to lower the risk of respiratory disease transmission in face-to-face, maskless meetings between individuals in a university setting. We evaluate overall aerosol transmission between users of the device. This is done for two different physical settings: a large space, such as a library, and a small space, such as an enclosed study room. We find that the device can provide lower aerosol transmission compared to the typical transmission between two individuals wearing surgical face masks separated by six feet. Full article