Search Results (143)

This paper examines how social movements influenced Greece’s macroeconomic adjustment during the financial crisis and austerity period (2010–2015). The purpose is to identify the channels through which mobilizations—anti-austerity protests, general strikes, youth actions, and solidarity networks—interacted with the economy. The main hypothesis is that social protest operates as an economic force via three mechanisms: expectations (shifts in household and firm beliefs affecting consumption, confidence, and investment), disruption (coordination and operating costs from strikes and stoppages affecting output and employment), and institutional feedback (policy sequencing and credibility under EU–IMF conditionality shaping behavior). Using a theoretical, literature-based methodology—a structured narrative review of peer-reviewed studies, policy documents, and historical syntheses—we map these mechanisms onto outcomes (GDP, unemployment, investment, consumer confidence). The findings support the hypothesis: expectations and feedback dominate the transmission to investment and confidence, while repeated disruption is most salient for labor-market dynamics; solidarity infrastructures cushion social costs but have ambiguous aggregate effects. The scope is interpretive and Greece-specific, yielding testable propositions for future causal work. Limitations follow from the design: the study does not estimate effect sizes or establish causality; conclusions are analytically persuasive rather than statistically demonstrative. The contribution is a mechanism map that integrates social-movement theory with crisis political economy and clarifies where empirical identification should focus. Full article

Remote sensing image dehazing presents substantial challenges in balancing physical fidelity with generative flexibility, particularly under complex atmospheric conditions and sensor-specific degradation patterns. Traditional physics-based methods often struggle with nonlinear haze distributions, while purely data-driven approaches tend to lack interpretability and physical consistency. To bridge this gap, we propose the Atmospheric Scattering Prior embedded Diffusion Model (ASPDiff), a novel framework that seamlessly integrates atmospheric physics into the diffusion-based generative restoration process. ASPDiff establishes a closed-loop feedback mechanism by embedding the atmospheric scattering model as a physics-driven regularization throughout both the forward degradation simulation and the reverse denoising trajectory. The framework operates through the following three synergistic components: (1) an Atmospheric Prior Estimation Module that uses the Dark Channel Prior to generate initial estimates of the transmission map and global atmospheric light, which are then refined through learnable adjustment networks; (2) a Diffusion Process with Atmospheric Prior Embedding, where the refined priors serve as conditional guidance during the reverse diffusion sampling, ensuring physical plausibility; and (3) a Haze-Aware Refinement Module that adaptively enhances structural details and compensates for residual haze via frequency-aware decomposition and spatial attention. Extensive experiments on both synthetic and real-world remote sensing datasets demonstrate that ASPDiff significantly outperforms existing methods, achieving state-of-the-art performance while maintaining strong physical interpretability. Full article

The exponential growth of multimedia communication necessitates advanced techniques for secure data transmission. This paper details a new data hiding method centered on a predictive voting mechanism that leverages neighboring pixels to estimate a pixel’s value. Secret data are concealed within these predictions via a purpose-built lookup table, and the retrieval process involves re-estimating the predicted pixels and applying an inverse mapping function. Experimental results demonstrate that the proposed method achieves an embedding capacity of up to 686,874 bits, significantly outperforming previous approaches while maintaining reliable data recovery. Compared with existing schemes, our approach offers improved performance in terms of both embedding capacity and extraction accuracy, making it an effective solution for robust multimedia steganography. Full article

Electroencephalography (EEG) communication technology with ultra-low power consumption, high transmission data rates, and low latency plays a significant role in mHealth, telemedicine, and Internet of Medical Things (IoMT). In this paper, generalized frequency division multiplexing (GFDM)-based direct mapping (DM) multi-input—multi-output (MIMO) mobile EEG communication technology (MECT) is proposed for implementation with the above-mentioned applications. The (2000, 1000) low-density parity-check (LDPC) code, four-quadrature amplitude modulation (4-QAM), a power assignment mechanism, and the 3rd Generation Partnership Project (3GPP) cluster delay line (CDL) channel model D were integrated into the proposed EEGCT. The transmission bit error rates (BERs), mean square errors (MSEs), and Pearson-correlation coefficients (PCCs) of the original and received EEG signals were evaluated. Simulation results show that, with a signal to noise ratio (SNR) of 14.51 dB, with a channel estimation error (CEE) of 5%, the BER, MSE, and PCC of the original and received EEG signals were 9.9777 × 10⁻⁸, 1.440 × 10⁻⁵ and 0.999999998, respectively, whereas, with an SNR of 15.0004 dB and a CEE of 10%, they were 9.9777 × 10⁻⁸, 1.4368 × 10⁻⁵, and 0.999999997622151, respectively. As the BER value, and PS saving are 9.9777 × 10⁻⁸, and 40%, respectively. With the CEE changes from 0% to 5%, and 5% to 10%, the N₀ values of the proposed MECT decrease by approximately 0.0022 and 0.002, respectively. The MECT has excellent EEG signal transmission performance. Full article

Background: Namibia is experiencing a generalized HIV epidemic, with 7.5% of the population living with HIV. Adolescent girls and young women (AGYW) aged 15–24 account for 28.6% of new infections annually. Various factors increase AGYW’s vulnerability to HIV. To address this, Project HOPE Namibia (PHN)-led consortium implemented the PEPFAR/USAID-funded DREAMS project in Khomas, Oshikoto, and Zambezi regions from 2018 to 2023. This study estimated the AGYW population most in need of HIV prevention and assessed geographic and age-specific gaps to improve program effectiveness and efficiency. Methods: This secondary data analysis utilized the Namibia Population-Based HIV Impact Assessment (NamPHIA) 2017, the Namibia census, and service data from the DREAMS project, which includes entry points for recruitment, screening, and enrolment. We used Python to conduct unadjusted and adjusted Poisson regression and UpSet plots for data visualization. Results: Analysis of NamPHIA data revealed low HIV prevalence in 10–14-year-olds, with only Oshikoto showing a detectable rate of 2.76%, mostly attributed to perinatal HIV transmission. Of the 12 DREAMS eligibility criteria, three could be mapped to 10–14-year-olds, while all except sexually transmitted infections could be mapped for 15–19 and 20–24-year-olds. Nationally, 17.3% of 10–14-year-old AGYW, 48.0% of 15–19-year-olds, and 50% of 20–24-year-olds met at least one DREAMS eligibility criterion. Among 15–19-year-olds, a history of pregnancy, no/irregular condom use, and out-of-school status were positively associated with HIV status. For 20–24-year-olds, transactional sex was positively associated with HIV status. Overall, 62% of screened individuals were eligible, and 62% of eligible individuals enrolled. PHN screened 134% of the estimated 37,965 10–14-year-olds, 95% of the estimated 35,585 15–19-year-olds, and 57% of the 24,011 20–24-year-olds residing in the five districts where DREAMS was implemented. Conclusions: We recommend the refinement of the DREAMS eligibility criteria, particularly for AGYW 10–14, to better identify and engage those at risk of HIV acquisition through sexual transmission. For 15–19-year-olds, PHN efforts should interrogate geographic variability in entry points for recruitment and screening practices. PHN should enhance the recruitment and engagement of AGYW 20–24, with a particular focus on those engaged in transactional sex. Full article

Hybridization of solar and wind energy sources is a promising solution to enhance the dispatch capability of renewables. The complementarity of wind and solar radiation, as well as the sharing of transmission lines and other infrastructures, can notably benefit the deployment of renewable power. Mapping of hybrid solar–wind potential can help identify new emplacements or existing power facilities where an extension with a hybrid system might work. This paper presents an analysis of a hybrid solar–wind potential by considering a reference power plant of 40 MW in the Iberian Peninsula and comparing the hybrid and non-hybrid energy generated. The generation of energy is estimated using SAM for a typical meteorological year, using PVGIS and ERA5 meteorological information as input. Modeling the hybrid plant in relation to individual PV and wind power plants minimizes the dependence on technical and economic input data, allowing for the expression of potential hybridization analysis in relative numbers through maps. Correlation coefficient and capacity factor maps are presented here at different time scales, showing the complementarity in most of the spatial domain. In addition, economic analysis in comparison with non-hybrid power plants shows a reduction of around 25–30% in the LCOE in many areas of interest. Finally, a sizing sensitivity analysis is also performed to select the most beneficial sharing between PV and wind. Full article

Understanding the dynamic carbon emission status is vital for turning a power system into a low-carbon system. However, the existing research has normally considered the average carbon emissions as the indicator for the operation and planning of power systems. Detailed carbon emission responsibility is not well allocated to different demands within power systems, leading to inefficient emission control. To address this problem, this paper develops a data-driven method for accurately finding the characteristics of the nodal marginal emission factor without the requirement of real-time optimal power flow (OPF) simulation. First, the nodal marginal emission factor system is derived based on actual data covering a timespan of one year on top of the IEEE 118 system. Then, a Graphical Neural Network (GNN) is adopted to map both the spatial and temporal relationship between nodal marginal emission and other features, thereby identifying the marginal emission characteristics for different nodes of power transmission systems. Through case studies, fine-tuned GNNs estimate all nodal marginal emission factor (NMEF) values for power systems without the requirement of OPF simulation and achieve a 5.75% Normalized Root Mean Squared Error (nRMSE) and 2.52% Normalized Mean Absolute Error (nMAE). Last but not least, this paper brings a new finding: a strong inclination to reduce marginal emission rates would compromise economic operation for power systems. Full article

While archipelagos have a demonstrated role in the stepping-stone process of the global dissemination of Aedes-borne viruses, they are often neglected in epidemiological and modelling studies. Over the past 20 years, some Atlantic archipelagos have witnessed a series of Aedes-borne viral outbreaks, prompting inquiries into the local historical suitability for transmission. In this study, the climate-based suitability for transmission of Aedes-borne viruses between 1980 and 2019 across Madeira, the Canaries, Cape Verde, and São Tomé e Príncipe archipelagos was estimated. For each island, we characterized the seasonality of climate-based suitability, mapped the spatial landscape of suitability, and quantified the historical effects of climate change. Results show that both island-level suitability and the historical impact of climate change decrease with distance from the equator, while significant seasonality patterns are observed only in subtropical climates. This study provides a unique historical perspective on the role of climate in shaping Aedes-borne virus transmission potential in Atlantic archipelagos. The findings herein described can inform local public health initiatives, including human-based prevention, targeted viral surveillance, and mosquito control programs. Full article

Localizing underwater nodes when they cannot be tethered or float on the surface presents significant challenges, primarily due to node mobility and the absence of fixed anchors with known coordinates. This paper advocates a strategy for tackling such a challenge by using visible light communication (VLC) from an airborne unit. A novel localization method is proposed where VLC transmissions are made towards the water surface; each transmission is encoded with the Global Positioning System (GPS) coordinates with the incident point of the corresponding light beam. Existing techniques deal with the problem in 2D by assuming that the underwater node has a pressure sensor to measure its depth. The proposed method avoids this limitation and utilizes the intensity of VLC signals to estimate the 3D position of the underwater node. The idea is to map the light intensity at the underwater receiver for airborne light beams and devise an error optimization formulation to estimate the 3D coordinates of the underwater node. Extensive simulations validate the effectiveness of the proposed method and capture its performance across various parameters. Full article

Background: Mapping the local etiology and susceptibility of common pathogens causing complicated urinary tract infection (cUTI) is important for promoting evidence-based antimicrobial prescribing. Evaluating the prevalence of extended-spectrum beta-lactamase (ESBL), AmpC beta-lactamase (AmpC), and carbapenemase-producing Enterobacterales (CPEs) is equally important as it informs treatment guidelines and empiric management. Whole genome sequencing (WGS) enhances antimicrobial resistance (AMR) surveillance by complementing phenotypic antimicrobial susceptibility testing, offering deeper insights into resistance mechanisms, transmissions, and evolutions. Integrating it into routine AMR monitoring can significantly improve global efforts to combat antimicrobial resistance. Methods: Antimicrobial susceptibility profiles of isolates from cUTI were collected from patients presenting with Sultan Qaboos University Hospital, Muscat and Suhar Hospital, Suhar, Oman. Automated systems as well as manual methods were used for detection of ESBL, AmpC, and CPE. ESBLs, AmpC β-lactamases, and CPEs were further detected by manual methods: double-disk synergy test for ESBL; disk approximation assay and D69C AmpC detection set for AmpC, and mCIM and KPC/IMP/NDM/VIM/OXA-48 Combo test kit for CPE. WGS was carried out in 11 FOX-resistant E. coli and (22 carbapenem-resistant K. pneumoniae) isolates with varying susceptibilities to identify circulating clades, AMR genes, and plasmids. Bioinformatic analysis was performed using online tools. Results: The susceptibility patterns of E. coli from cUTI were as follows: nitrofurantoin (96%), fosfomycin (100%), fluoroquinolones (44%), aminoglycosides (93%), piperacillin-tazobactam (95%), and carbapenems (98%). In comparison, susceptibility rates of K. pneumoniae were far lower: nitrofurantoin (38%), fosfomycin (89%), aminoglycosides (82%), piperacillin-tazobactam (72%), and carbapenems (83%). K. pneumoniae, however, was more susceptible to fluoroquinolones at 47% in comparison to E. coli. The prevalence of ESBL among E. coli and K. pneumoniae was 37.2% and CRE was 6.2% while the estimated prevalence of AmpC was 5.4%. It was observed that E. coli was the predominant ESBL and AmpC producer, while K. pneumoniae was the major carbapenem-resistant Enterobacterales (CREs) producer. No predominant multi-locus sequence typing (MLST) lineage was observed in AmpC-producing E. coli with nine E. coli MLST lineages being identified from eleven isolates: ST-10, ST-69, ST-77, ST-131, ST-156, ST-167, ST-361, ST-1125, and ST-2520. On the other hand, a less diverse MLST spectrum (ST-2096, ST-231, ST-147, ST-1770, and ST-111) was observed in the CRE K. pneumoniae. Among the five MLST lineages, ST-2096 (twelve isolates) and ST-147 (seven isolates) predominated. WGS revealed that DHA-1 was the predominant plasmid-mediated AmpC gene in E. coli, while OXA-232 and NDM-5 were the most common carbapenemase genes in K. pneumoniae. All E. coli DHA-1-positive isolates co-harbored the quinolone resistance gene qnrB4 and the sulfonamide resistance gene sul1 while no aminoglycoside resistance genes were detected. The majority of CPE CRE K. pneumoniae carried other β-lactamase genes, such as blaCTX-M-15, blaSHV, and blaTEM; all co-harbored the quinolone resistance gene OqxAB; and 77% carried the aminoglycoside resistance gene armA. Conclusions: Our results suggest that fosfomycin is an excellent empiric choice for treating complicated cystitis caused by both E. coli and K. pneumoniae, while nitrofurantoin is an appropriate choice for E. coli cystitis but not for K. pneumoniae. Aminoglycosides and piperacillin-tazobactam are excellent intravenous alternatives that spare carbapenems. DHA-1 was the predominant AmpC in E. coli, while OXA-232 and NDM-5 were the predominant carbapenemases in K. pneumoniae. In AmpC-producing E. coli, no MLST predominated, suggesting a significant flux in E. coli with lack of stable clades in this region. In contrast, ST-2096 and ST-147 predominated in CRE Klebsiella pneumoniae, suggesting a stable circulation of these in Oman. WGS profiling provides a deeper understanding of the genetic basis of resistance and enhances surveillance and offers comprehensive insights into pathogen evolution and transmission patterns. Full article

Background: GABAergic signaling plays a crucial role in modulating neuronal proliferation, migration, and the formation of neural network connections. The termination of GABA transmission primarily occurs through the action of GABA transporter 1 (GAT1), encoded by the SLC6A1 gene. Multiple SLC6A1 mutations have been implicated in neurodevelopmental disorders, but their effects on the nervous system are unclear. Methods: We estimated the expression pattern of the GAT1 (S295L) protein using the Slc6a1^S295L/S295L mouse model via RT-PCR, Western blotting, and confocal immunofluorescence. The effect of GAT1 (S295L) on hippocampal neurogenesis was investigated by neuronal marker staining (Sox2, Tbr2, NeuroD1, DCX, NeuN) and BrdU label experiments. The dendritic complexity was mapped through Sholl analysis. RNA-Seq was utilized to explore the signaling pathways and molecules associated with neurodevelopmental disorders. Results: We detected a remarkable decline in the quantity of type-2b intermediate progenitor cells, neuroblasts, and immature neurons in the dentate gyrus (DG) of Slc6a1^S295L/S295L mice at 4 weeks. These abnormalities were exacerbated in adulthood, as evidenced by compromised dendritic length and height as well as the complexity of immature neurons. Immunofluorescence staining showed the abnormal aggregation of GAT1 (S295L) protein in neurons. RNA-seq analysis identified pathways associated with neurodevelopment, neurological disorders, protein homeostasis, and neuronutrition. The neurotrophin Bdnf decreased at all ages in the Slc6a1^S295L/S295L mice. Conclusions: Our data provide new evidence that GAT1 (S295L) causes impaired neurogenesis in the DG. GAT1 mutation not only disrupts GABA homeostasis but also impairs the neurotrophic support necessary for normal hippocampal development, which may be one of the factors contributing to impaired neurogenesis. Full article

As global warming continues to intensify, typhoon disasters will more frequently occur in East and Southeast Asia, posing a high risk of causing large-scale power outages in the power system. To investigate the impact of typhoon disasters on high-voltage power grids, a comprehensive risk assessment method that integrates model-driven and data-driven approaches is proposed, which can predict power grid faults in advance and provide support for power grid operators to generate emergency dispatching plans. Firstly, by comparing actual loads with the design strengths of the transmission tower-line system and analyzing the geometric relationship between typhoon wind circles and the system, key variables, such as wind speed, longitude, latitude, and other pertinent factors, are screened. The Spearman correlation coefficient is employed to pinpoint the meteorological variables that exhibit a high degree of relevance, enhancing the accuracy and interpretability of our model. Secondly, addressing the lack of power grid fault samples, three data balancing methods—Borderline-SMOTE, ADASYN, and SMOTE-Tomek—are compared, with Borderline-SMOTE selected for its superior performance in enhancing the sample set. Additionally, a power grid failure risk assessment model is built based on Light Gradient Boosting Machine (LightGBM), and the Borderline-Smoothing Algorithm (BSA) is used for the modeling of power grid faults. The nonlinear mapping relationship between typhoon meteorological data and the power grid equipment failure rate is extracted through deep learning training. Subsequently, the Tree-structured Parzen Estimator (TPE) is leveraged to optimize the hyperparameters of the LightGBM model, thus enhancing its prediction accuracy. Finally, the actual power system data of a province in China under a strong typhoon are assessed, validating the proposed assessment method’s effectiveness. Full article

The presence of dead broilers within a flock can be significant vectors for disease transmission and negatively impact the overall welfare of the remaining broilers. This study introduced a dead broiler detection method that leverages the fact that dead broilers remain stationary within the flock in videos. Dead broilers were identified through the analysis of the historical movement information of each broiler in the video. Firstly, the frame difference method was utilized to capture key frames in the video. An enhanced segmentation network, YOLOv8-SP, was then developed to obtain the mask coordinates of each broiler, and an optical flow estimation method was employed to generate optical flow maps and evaluate their movement. An average optical flow intensity (AOFI) index of broilers was defined and calculated to evaluate the motion level of each broiler in each key frame. With the AOFI threshold, broilers in the key frames were classified into candidate dead broilers and active live broilers. Ultimately, the identification of dead broilers was achieved by analyzing the frequency of each broiler being judged as a candidate death in all key frames within the video. We incorporated the parallelized patch-aware attention (PPA) module into the backbone network and improved the overlaps function with the custom power transform (PT) function. The box and mask segmentation mAP of the YOLOv8-SP model increased by 1.9% and 1.8%, respectively. The model’s target recognition performance for small targets and partially occluded targets was effectively improved. False and missed detections of dead broilers occurred in 4 of the 30 broiler testing videos, and the accuracy of the dead broiler identification algorithm proposed in this study was 86.7%. Full article

The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the very-high-frequency (VHF) data exchange system (VDES). While the VDES increases bandwidth and data rates, ensuring the stable transmission of maritime IoT (MIoT) application data in congested coastal areas remains a challenge due to frequent collisions of AIS messages. This paper presents a slot occupancy-based collision avoidance algorithm (SOCA) for a VDES network in the MIoT. SOCA is designed to mitigate the impact of interference caused by transmissions of AIS messages on transmissions of VDE-Terrestrial (VDE-TER) data in coastal areas. To this end, SOCA provides four steps: (1) construction of the neighbor information table (NIT) and VDES frame maps, (2) construction of the candidate slot list, (3) TDMA channel selection, and (4) slot selection for collision avoidance. SOCA operates by constructing the NIT based on AIS messages to estimate the transmission intervals of AIS messages and updating VDES frame maps upon receiving VDES messages to monitor slot usage dynamically. After that, it generates a candidate slot list for VDE-TER channels, classifying the slots into interference and non-interference categories. SOCA then selects a TDMA channel that minimizes AIS interference and allocates slots with low expected occupancy probabilities to avoid collisions. To evaluate the performance of SOCA, we conducted experimental simulations under static and dynamic ship scenarios. In the static ship scenario, SOCA outperforms the existing VDES, achieving improvements of 13.58% in aggregate throughput, 11.50% in average latency, 33.60% in collision ratio, and 22.64% in packet delivery ratio. Similarly, in the dynamic ship scenario, SOCA demonstrates improvements of 7.30%, 11.99%, 39.27%, and 11.82% in the same metrics, respectively. Full article

Recent research in solid-state physics and materials engineering focuses on the development of new dielectric materials, with bismuth-based pyrochlores being already extensively applied in communications technology for their excellent dielectric properties and relatively low sintering temperatures. Herein, the structural, morphological, electrical, and magnetic properties of Bi_1.34Fe_0.66Nb_1.34O_6.35 ceramic, prepared by the sol–gel method and sintered at 500 °C, are investigated. The Rietveld refinement of the XRD pattern showed a cubic phase belonging to the space group Fd-3m and a crystallite size of 42 nm. Transmission electron microscopy further confirmed the crystallite size and the homogeneous distribution of Bi, Fe, Nb, and O elements, as evidenced by high-angle annular dark field imaging and STEM-EDX mapping. The morphology of the sample, assessed by scanning electron microscopy, is characterized by submicron-sized spherical particles. Dielectric spectroscopic studies revealed that the dielectric properties, strongly influenced by frequency and temperature, indicate the material’s potential for energy storage due to lower dielectric loss compared to the dielectric constant. The observed relaxation phenomena, confirmed through variations in dielectric loss and loss tangent, highlight the influence of grain boundaries and temperature on electron hopping and charge carrier dynamics. Using SQUID magnetometry, we identified two distinct magnetic phases. The primary phase, corresponding to the Bi_1.34Fe_0.66Nb_1.34O_6.35 ceramic, exhibits an antiferromagnetic behavior below its Néel temperature at around 8.8 K. A secondary high-Curie temperature ferrimagnetic phase, likely vestigial maghemite and/or magnetite, was also detected, indicating an estimated fraction below 0.02 wt.%. Full article