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Don't Stand So Close to Me: Foreign and Domestic Incumbents and New Business Births
Get PDFWe examine the spatial pattern of agglomeration effects on firm births, distinguishing between domestic and foreign ownership among incumbent firms. Using data from Polish municipalities, our analysis considers both domestic and foreign firm births, capturing neighbourhood effects with spatial lags. We argue that the spatial pattern of agglomeration effects results from a combination of competition for resources and positive externalities, where the latter are mostly associated with foreign incumbents. We find that foreign incumbents in the neighbourhood have a stronger positive association with firm births than those in the focal municipality; in contrast, the reverse pattern applies to domestic incumbents
Hepatitis B Surface Antigen Loss and Improved Clinical Outcomes in Asians with Chronic Hepatitis B Virus Infection
Get PDFBACKGROUND AND AIMS: Chronic hepatitis B virus (HBV) infection accounts for substantial disease burden and mortality due to liver complications. Hepatitis B surface antigen (HBsAg) loss is a key component of functional cure when assessing treatment efficacy. However, the impact of HBsAg loss on clinical outcomes deserves further exploration. METHODS: This population-based cohort study used electronic health record data from a territory-wide database in Hong Kong to identify patients with chronic HBV infection (2005-2019). The association between HBsAg loss and outcomes was assessed: compensated cirrhosis, decompensated liver disease (DLD), hepatocellular carcinoma (HCC), and all-cause mortality (ACM). A marginal structural model using inverse probability weighting was used to estimate hazard ratios (HRs; 95% confidence interval [CI]) adjusted for time-fixed and time-varying confounders. Health-care resource utilization before and after loss was evaluated. RESULTS: The study population comprised 71,077 patients accruing 348,379 person-years; 1639 (2.3%) experienced HBsAg loss, which occurred with a mean (standard deviation) of 74.63 (37.5) months after chronic HBV index date. HBsAg loss was associated with a reduced risk of DLD (74%; HR 0.26 [95% CI 0.08-0.83]), HCC (66%; 0.34 [0.19-0.61]), and ACM (26%; 0.74 [0.57-0.97]). The HR for compensated cirrhosis was 0.57 (0.30-1.14). Each additional month of HBsAg loss was associated with decreased risk of HCC and ACM. Of those experiencing HBsAg loss, cumulative probability of persistence at 24 and 60 months was 99% and 97%, respectively. Hospital admission, inpatient days, and drug prescribing were higher before HBsAg loss versus 6, 12, and 24 months post-HBsAg loss. CONCLUSION: In this large population-based study with extended follow-up in Hong Kong, HBsAg loss was associated with reduced risk of DLD, HCC, and ACM
Ethnic dealignment and the limits of representation: South Asian Muslim voting behaviour under Keir Starmer
Get PDFThe 2024 UK General Election produced the most ethnically diverse Parliament in history, with approximately 14 per cent of MPs from minority backgrounds, closely reflecting the electorate. Yet support for the Labour Party among some ethnic minority communities, particularly South Asian Muslims, has begun to wane. Drawing on a longitudinal ethnographic study of interviews in Bradford across the 2015 and 2024 General Elections, this article examines why these shifts appear to be accelerating under Keir Starmer’s leadership and how perceptions of representation shape political behaviour. The findings show that descriptive and symbolic representation, namely, visible diversity without substantive advocacy, alone do not secure voter loyalty: voters remain aligned with political parties only when they perceive the party acts in their material and political interests. These patterns indicate an emerging ethnic dealignment, analogous to the class dealignment that transformed British politics in the late twentieth century. While Labour retains strong support among Black Britons and other minorities, disaffection among South Asian Muslims, shaped largely by the party’s stance on Gaza, exposes new vulnerabilities. Placing these developments within a broader framework of political dealignment, the article demonstrates how ethnic diversity is reshaping the social foundations of centre-left politics in Britain, with lessons for parties across Europe
Comprehensive Physicochemical Investigation of a Water-Soluble Adduct of C70 with l-Methionine and l-Cysteine
No full textWe study the physicochemical and biological properties of water-soluble adducts of fullerene C70 with l-methionine (C70-Met, C70(C5H11NO2S)3) and l-cysteine (C70-Cys, C70(C3H7NO2S)3). The adducts were characterized using 13C NMR, IR, and UV spectroscopy, elemental analysis, and HPLC. The measured physicochemical properties included temperature and concentration dependence of density, viscosity, refraction, electrical conductivity, surface tension of aqueous solutions, solubility in binary systems (C70-Met–H2O and C70-Cys–H2O) and ternary systems (C70-Met–NaCl–H2O and C70-Cys–NaCl–H2O), as well as determination of the partition coefficient in an n-octan-1-ol–water system. In addition, investigation of binding to human serum albumin and DNA was conducted as well as antiradical activity in the model reaction with a stable 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) was studied
Surface engineering and nanomaterials for sustainable indirect evaporative cooling systems
Get PDFThe rising demand for energy-efficient cooling has driven advancements in Indirect Evaporative Cooling (IEC) systems. This review explores nanomaterials and surface enhancements to improve thermal efficiency, corrosion resistance, and anti-fouling properties. Nanomaterials can boost COP by 16–43% and enable up to 36.1% energy savings. However, scalability and stability challenges remain. Emerging trends like MOFs and additive manufacturing offer promising solutions, guiding future research toward large-scale, sustainable IEC applications
Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
No full textReservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite growing interest, a comprehensive evaluation that quantifies the impact of these factors is still lacking. This paper systematically compares various preprocessing methods for memristive RC systems, assessing their effects on accuracy and energy consumption. We also propose a parity-based preprocessing method that improves accuracy by 2-6% while requiring only a modest increase in device count compared to other methods. Our findings highlight the importance of informed preprocessing strategies to improve the efficiency and scalability of memristive RC systems
AI-enhanced simulation for sustainable production in pulp and paper industry
Get PDFThis study investigates how environmental policies influence production planning in environmentally sensitive manufacturing systems, particularly in the paper and pulp industry. Despite growing regulatory pressure and consumer awareness, existing research often overlooks the integration of environmental policies with operational uncertainty. To address this gap, we propose the Environmental Hedging Point Policy (EHPP) as a strategic framework that draws on optimal control theory to dynamically balance sustainability and operational performance under uncertainty. Our approach combines simulation-based optimization with multi-objective particle swarm optimization and K-means clustering to evaluate trade-offs between cost, customer satisfaction, and environmental impact. We model a dynamic demand environment shaped by eco-conscious customer preferences and test policy scenarios using data from a paper manufacturing system involving both recyclable and virgin paper inputs. The results provide actionable insights for policymakers and manufacturers, supporting sustainable production planning under uncertainty
A Data-Drive Servitization Intensity Typology for UK Manufacturing: Clusters, Stage-Specific Enablers and Predictive Modelling
No full textWhen RNN Meets CNN and ViT: The Development of a Hybrid U-Net for Medical Image Segmentation
Get PDFDeep learning for semantic segmentation has made significant advances in recent years, achieving state-of-the-art performance. Medical image segmentation, as a key component of healthcare systems, plays a vital role in the diagnosis and treatment planning of diseases. Due to the fractal and scale-invariant nature of biological structures, effective medical image segmentation requires models capable of capturing hierarchical and self-similar representations across multiple spatial scales. In this paper, a Recurrent Neural Network (RNN) is explored within the Convolutional Neural Network (CNN) and Vision Transformer (ViT)-based hybrid U-shape network, named RCV-UNet. First, the ViT-based layer was developed in the bottleneck to effectively capture the global context of an image and establish long-range dependencies through the self-attention mechanism. Second, recurrent residual convolutional blocks (RRCBs) were introduced in both the encoder and decoder to enhance the ability to capture local features and preserve fine details. Third, by integrating the global feature extraction capability of ViT with the local feature enhancement strength of RRCBs, RCV-UNet achieved promising global consistency and boundary refinement, addressing key challenges in medical image segmentation. From a fractal–fractional perspective, the multi-scale encoder–decoder hierarchy and attention-driven aggregation in RCV-UNet naturally accommodate fractal-like, scale-invariant regularity, while the recurrent and residual connections approximate fractional-order dynamics in feature propagation, enabling continuous and memory-aware representation learning. The proposed RCV-UNet was evaluated on four different modalities of images, including CT, MRI, Dermoscopy, and ultrasound, using the Synapse, ACDC, ISIC 2018, and BUSI datasets. Experimental results demonstrate that RCV-UNet outperforms other popular baseline methods, achieving strong performance across different segmentation tasks. The code of the proposed method will be made publicly available
A Solution Procedure for Fixed Mammography Unit Location-Allocation and Mobile Mammography Unit Routing Problems
Get PDFThis paper addresses the Mammography Unit Location-Allocation and Mobile Mammography Unit Routing problems. The objective is to maximize coverage of the target population and cover unmet demand with fixed mammography units by using mobile units. It is proposed a sequential solution procedure for solving, in which the first problem is solved by using an exact method, and the second one through a heuristic algorithm with the uncovered municipalities from the first problem as input. This proposal was tested in three scenarios from the State of Minas Gerais, Brazil. The results show that the coverage of this state can be fully met with 84 additional mobile units, considering the current location of the fixed equipment and the restriction of the municipalities’ service to their healthcare micro-regions. However, if this requirement is not imposed, 42 units are sufficient. Finally, by allowing the equipment to be relocated, only nine units are needed
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