Hydrological imbalance in Nam Co Lake, the third-largest lake on the Tibetan Plateau

Lakes on the Tibetan Plateau (TP), as key components of the Asian Water Tower, play a vital role in regional water resources and hydrologic cycle. Groundwater is an important but often ignored factor in the lake’s hydrologic cycle, largely due to challenges in quantifying its contribution. In the case of the Nam Co Lake, the third-largest lake on the TP, research on basin groundwater remains notably limited. This paper presents, for the first time, an analysis of groundwater in the water balance of Nam Co Lake, based on a comprehensive hydrogeological investigation. Results revealed lacustrine groundwater discharge into the lake was about 10.31 ± 0.31 × 108 m3 during May to October 2018. Daily lake level change showed that the rising lake level resulted in a volume increase of 13.06 ± 1.53 × 108 m3 during the same period. The hydrometeorological observations revealed that during the observation period, precipitation over the lake, recorded by the automatic weather station, totaled 7.51 ± 0.75 × 108 m3, while evaporation from the lake, measured by the eddy covariance system, amounted to 9.56 ± 0.12 × 108 m3. Additionally, runoff of surrounding rivers into the lake reached 22.83 ± 2.28 × 108 m3. Thus, a lake water balance analysis revealed a surplus input of 18.03 ± 2.42 × 108 m3 compared to the output during the water balance duration. The only plausible explanation for the water imbalance is seepage, most likely occurring along the region’s known subsurface fault system. These findings underscore the significant role of groundwater and highlight the magnitude of lake seepage, offering new insights into the hydrological cycle of Nam Co Lake and the broader TP region

Breast cancer-related fatigue risk and intervention recommendations:What can and cannot be personalised?

Cancer-related fatigue (CRF) is one of the most common and underdiagnosed long-term effects after breast cancer. Many factors influence the development of CRF, however, on individual level it is unknown who is going to develop CRF. There are many interventions to reduce CRF, but unfortunately, not a gold-standard intervention that works best for all patients. These both aspects need personalisation, and so the goal of this thesis was to determine what can and cannot be personalised in risks for fatigue and intervention recommendations for breast cancer-related fatigue.In Chapter 2, we studied the personalisation of the risk of developing CRF. It was not possible to accurately predict CRF, as CRF is a complex construct. So, in Chapter 3, focus groups with patients and interviews with healthcare professionals showed the complexity of CRF and the factors that are important to CRF.In Chapter 4, an overview of existing interventions was created, to show the large variation and possibility to give a personalised intervention recommendation. In Chapter 5, breast cancer patients indicated their preferences for interventions and decision rules were developed to create a simple personalised intervention recommendation. In Chapter 6, we tried to predict intervention effectiveness on individual level, again to further personalise the intervention recommendation. As in Chapter 2, it showed to be difficult to predict CRF.In the general discussion of Chapter 7, two themes emerged: the personalisation in predictions in fatigue, and the personalisation of intervention recommendations. For the first theme, improvements of the work of this thesis lies in either the data used, or the modelling approaches. For the second theme, we dived into the extension of the decision rules, and how patients can still receive a personalised intervention recommendation. Future research should focus on the standardisation of data to have a common method to measure CRF, if this is possible at all, and the implementation of the results of this thesis into clinical practice.It can be concluded that based on current available data, personalisation in predictions in fatigue is not accurately possible, while in the personalisation of intervention recommendations, first important steps were made

Positivity effects in self-defining memories in men and women across adulthood:different patterns between self-rated affect and content-coded meaning

The positivity effect (PE) refers to older adults’ selective attention and memory to positive over negative information. Older adults often rate their personal memories more positively and less negatively than younger people. However, findings are mixed when memory content is analysed. This study examined the PE using self-report and content-coded measures in self-defining memories (SDMs) and the role of gender in moderating the PE. A representative sample (N = 1985; 18–92 years) reported three SDMs and rated positive and negative affect toward each memory on three occasions within the one-year interval. Each memory was coded for positive and negative meaning-making. Memory valence was also coded to determine positive and negative SDMs. Multilevel analyses showed that age predicted greater positive and lower negative affect. Mixed findings emerged when meaning-making was featured. Age predicted lower positive and lower negative meaning-making in negative SDMs. Gender moderated the PE. Women showed greater age-related negativity reduction than men in negative SDMs assessed by self-rated affect. While women presented greater negative meaning-making in negative SDMs than men, the gap converged in older age. These findings were controlled for mental health symptoms. Together, this study suggests that how SDMs are felt and narrated may be two distinct processes.</p

Ultra-Low-Power Dynamic-Bias Comparators With Self-Clocked Latch in 65-nm CMOS

This article introduces two comparators featuring a dynamic-bias preamplifier and self-clocked latches, tailored for ultra-low-power and medium-speed applications with &lt;500-µV input-referred noise (IRN). The proposed self-clocked latches are activated by the preamplifier outputs and therefore operate with a lower common-mode current, which in turn minimizes the crowbar current that is typically present during the comparison phase in conventionally clocked latches. The proposed comparators do not require an additional clock phase for the latch, thereby reducing the clock drive power consumption for systems employing multiple latches, e.g., in ADCs or memories. The absence of an additional clock phase for latching makes them robust toward clock skews, similar to the “StrongARM comparator.” This article also presents an exhaustive overview of the prior art, the “Schinkel comparator,” and its bottlenecks when optimizing for low-noise low-power applications, thereby motivating the importance of self-clocked latches for such applications. This article also discusses the noise–energy–delay design tradeoffs of the proposed dynamic-bias self-clocked (DBSC) comparators. Fabricated in a 65-nm CMOS process along with a standard Schinkel comparator, the two proposed designs exhibit an IRN of 320 and 460 µV while consuming approximately 40fJ of energy per comparison from a 1.2-V supply. The measured CLK-OUT delay stands roughly at 0.5 ns. The results indicate a 2 × enhancement in energy efficiency and a 3.7 × and 2.6 × reduction in IRN with a 2.5 × increment in CLK-OUT delay for similar differential input voltages when compared to the “Schinkel comparator.”</p

Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models

Vision-Language Models (VLMs) learn a shared feature space for text and images, enabling the comparison of inputs of different modalities. While prior works demonstrated that VLMs organize natural language representations into regular structures encoding composite meanings, it remains unclear if compositional patterns also emerge in the visual embedding space. In this work, we investigate compositionality in the image domain, where the analysis of compositional properties is challenged by noise and sparsity of visual data. We address these problems and propose a framework, called Geodesically Decomposable Embeddings (GDE), that approximates image representations with geometry-aware compositional structures in the latent space. We demonstrate that visual embeddings of pre-trained VLMs exhibit a compositional arrangement, and evaluate the effectiveness of this property in the tasks of compositional classification and group robustness. GDE achieves stronger performance in compositional classification compared to its counterpart method that assumes linear geometry of the latent space. Notably, it is particularly effective for group robustness, where we achieve higher results than task-specific solutions. Our results indicate that VLMs can automatically develop a human-like form of compositional reasoning in the visual domain, making their underlying processes more interpretable. Code is available at https://github.com/BerasiDavide/vlm_image_compositionality

Driving business performance through green procurement policy:The power of supply chain information sharing for robust supply chain

Few researchers seek to understand how firms can drive business performance through green procurement policy. Fewer still embrace the power of supply chain information sharing for robust supply chain management. If this is so, then there is a cause for concern. Here, the researchers used SmartPLS4 for data analysis and model testing, utilizing the mediating effects of supply chain resilience and supply chain robustness in the relationship between green procurement policy and business performance. The researchers found a positive association between supply chain information sharing, entrepreneurial orientation proactiveness, and green procurement policy. The results signify that supply chain resilience and supply chain robustness mediate the constructive association between green procurement policy and business performance. Our findings have practical implications where firms can promote information flow, foster proactive entrepreneurship, and enhance supply chains’ resilience and robustness to support green procurement strategies. By integrating these insights into their strategy, businesses can gain a competitive advantage in the global market while fulfilling their regulatory requirements and stakeholder expectations. Our research is the first to examine the mediating role of supply chain resilience and supply chain robustness in ensuring the impact of green procurement policy on enhancing business performance.</p

Accessibility to cancer medicines in Europe:Towards equitable access and fair pricing

This dissertation explores disparities in access to novel cancer medicines across Europe from the perspectives of patients, healthcare providers, and national authorities. It addresses challenges related to accessibility, pricing, and the socioeconomic impact of cancer, with the aim of informing evidence-based policies to support fair and sustainable cancer care.In the first part, access disparities were assessed through surveys with hospital pharmacists and oncology specialists across several European countries. Findings revealed significant variation in time to access and availability of novel cancer medicines, influenced by hospital type, country-specific regulations, and medicine type. Combination therapies were generally less accessible than monotherapies, and access was often faster in specialized hospitals and countries with strong early access programs. The second part of the dissertation examined medicine pricing. We analyzed managed entry agreements (MEAs), revealing differences in how countries negotiate and report discounts. Although MEAs aim to reduce costs, confidentiality limits transparency and comparability. We also presented confidential hospital-level pricing data from nine countries, showing wide disparities and emphasizing the need for standardized reporting to strengthen negotiations and collaboration.The third part investigated the socioeconomic impact of cancer from the patient perspective through the SEC study, involving over 3,000 patients in 25 countries. High levels of financial toxicity were reported, particularly among younger, lower-income, self-employed, and less-educated patients. A sub-study on adolescents and young adults (AYAs) found that 79% experienced financial difficulties, with limited support tailored to their needs. Many healthcare professionals remained unaware of these challenges.The dissertation concludes with six key recommendations, including harmonized EU regulations for early access, improved use of real-world evidence, discontinuation of external reference pricing, and enhanced price transparency. It also calls for the development of validated tools to identify vulnerable patients and inform targeted, supportive policies across Europe

Super-Vth Standard Cells With Improved EDP:Design and Silicon Validation in 65nm LP CMOS

The ever-increasing computational load and shrinking power budget have accentuated the need for energy-efficient operation of edge devices. In this article, a combination of static CMOS logic and Hybrid Pass transistor logic with Static CMOS output (HPSC), which has no floating or weak nodes and is thus as robust to noise as static CMOS logic, is used for designing toolchain-compatible super-Vth standard cells. Optimized HPSC variants of a 2/3-input XOR cell, a 2/3-input XNR cell, a half adder cell, a full adder cell, and two variants of a 1-bit multiply-accumulate combinational cell are presented in a commercial 65nm Low-Power CMOS technology. Measurements of test structures based on ring oscillators and dummy path techniques show an average frequency and average energy-delay product improvement of up to 30.3% and 32.5% respectively at typical conditions. The proposed cells’ superior performance compared to the commercially available standard cells is also highlighted in terms of propagation delay, leakage, and dynamic power consumption. This shows a promising approach for foundries or other commercial entities to improve digital design performance to about half a technology node at no additional cost

Estimating soil organic carbon using time series Band 11 (SWIR) of multispectral Sentinel-2 satellite images and machine learning algorithms

Soil Organic Carbon (SOC) is a critical soil property impacting food security and climate change. Traditional methods for SOC estimation are time-consuming, expensive, and unsuitable for large-scale application. Consequently, researchers have increasingly focused on utilizing Remote Sensing (RS) images for SOC estimation over the past two decades. However, achieving high SOC estimation accuracy (more than 80 %) remains challenging. This limitation often stems from a mismatch between the complexity of SOC and the information captured by traditional RS observations (e.g., reflectance bands or spectral indices), as conventional feature extraction methods from RS images may not be detailed enough to monitor the many factors influencing SOC concentration. One promising solution to enhance feature extraction is the use of time series observations, analyzing multiple images over time instead of relying on single-time images. This study proposes a novel approach leveraging time series of the Sentinel-2 satellite's B11 band (centered around 1610 nm, a region sensitive to SOC absorption features) along with Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to extract more meaningful temporal features. Specifically, ten new features based on temporal variations were derived by applying PCA and ICA to the B11 band time series images. These temporal features were then combined with features derived from the median of all Sentinel-2 images acquired during the summer of 2019, corresponding to the soil data collection period. Four machine learning algorithms (RF, GBRT, XGBoost, and LightGBM) were employed across four distinct scenarios to evaluate the novel feature extraction method and a feature selection algorithm. The scenarios were designed as follows: Scenario one (S#1) and Scenario two (S#2) did not utilize the time series features, while Scenario three (S#3) and Scenario four (S#4) did. A binary Genetic Algorithm (GA) for feature selection was implemented in S#2 and S#4, distinguishing them from S#1 and S#3 respectively. XGBoost performed best, achieving an R2 of 0.891 in S#4 (time series features and GA). Incorporating time series features significantly improved accuracy by 0.11, while GA-based feature selection added another 0.05. The findings highlight the effectiveness of the developed feature extraction algorithm, using Sentinel-2's B11 time series and advanced transformations, for substantially improving SOC level estimation

Science-media relationships in times of crisis and transformation

Contemporary grand challenges, including climate change, pandemics, and technological disruptions, require coordinated responses from science and society. Science journalism plays a critical role in meeting the societal need for scientific information, yet relationships between researchers and journalists are undergoing profound changes. Therefore, this dissertation examines what challenges arise from contemporary changes in science-media relationships and what the implications are for the roles and responsibilities of researchers and journalists. Changing media landscapes result in declining funding and increasing competition and time pressure for science journalists. Simultaneously, due to medialisation of science, researchers and research institutes are increasingly communicating directly and strategically with non-expert audiences through press releases and (social) media. Together, these trends make journalists more dependent on the information that research organisations provide, raising concerns about the independence and critical nature of science journalism.These issues became especially visible during the COVID-19 pandemic, when there was an urgent need for accurate information, and scientists and journalists had to work closely together under pressure. Scientists struggled to balance their responsibilities to inform and advise, while journalists had to stay critical and avoid becoming too close to their sources. At the same time, both parties relied heavily on model-based visualisations, such as flatten-the-curve graphs, to predict and illustrate the course of the pandemic. These visualisations require special attention, because they are difficult to explain and interpret correctly. Similar dynamics emerge in coverage of generative artificial intelligence (AI), which acts both as a subject of reporting and as a disruptive force that transforms journalistic practices. AI can help journalists with routine tasks but also raises concerns about rigour, accuracy and transparency in science journalism. Overall, the findings show that closer cooperation between scientists and journalists can increase mutual understanding, while it can also reduce independent and critical journalistic reporting. Amid rising misinformation and growing use of pre-prints and AI, assessing the quality of scientific information becomes increasingly complex. Thus, this dissertation reconsiders the roles and responsibilities of both researchers and journalists to provide insight into how they can contribute to trustworthy science journalism in times of crisis and transformation