Search Results (173)

Smart thermostats are a key technology for reducing residential energy consumption in smart cities, but their real-world effectiveness depends on the interaction between automation, occupant behavior, and the design of behavioral interventions. This study presents a physics-informed assessment of thermostat strategies across Luxembourg’s single-family home stock, using an aggregate thermal model calibrated to eight years of hourly national heating demand and meteorological data. We simulate five categories of behavioral scenarios: dynamic thermostat adjustments, heat-wasting window-opening behavior, flexible comfort models, occupancy-based automation, and a portfolio of four probabilistic nudges (social comparison, real-time feedback, pre-commitment, and gamification). Results show that occupancy-based automation delivers the largest energy savings at 12.9%, by aligning heating with presence. In contrast, behavioral savings are highly fragile, as a stochastic window-opening behavior significantly erodes the 9.8% savings from eco-nudges, reducing the net gain to 7.6%. Among nudges, only social comparison yields significant savings, with a mean reduction of 7.6% (90% confidence interval: 5.3% to 9.8%), by durably lowering the thermal baseline. Real-time feedback and pre-commitment fail, achieving less than 0.5% savings, because they are misaligned with high-consumption periods. Thermal comfort, the psychological state of satisfaction with the thermal environment drives a large share of residential energy use. These findings demonstrate that effective smart thermostat design must prioritize robust, presence-responsive automation and interventions that reset default comfort norms, offering scalable, policy-ready pathways for residential energy reduction in urban energy systems. Full article

Personalized cancer vaccines are a key strategy for training the immune system to recognize and respond to tumor-specific antigens. Our earlier software release, AutoEpiCollect 1.0, was designed to accelerate the vaccine design process, but the identification of tumor-specific genetic variants remains a manual process and is highly burdensome. In this study, we introduce AutoEpiCollect 2.0, an improved version with integrated genetic analysis capabilities that automate the identification and prioritization of tumorigenic variants from individual tumor samples. AutoEpiCollect 2.0 connects with RNA sequencing and cross-references the resulting RNAseq data for efficient determination of cancer-specific and prognostic gene variants. Using AutoEpiCollect 2.0, we conducted two case studies to design personalized peptide vaccines for two distinct cancer types: cervical squamous cell carcinoma and breast carcinoma. Case 1 analyzed five cervical tumor samples from different stages, ranging from CIN1 to cervical cancer stage IIB. CIN3 was selected for detailed analysis due to its pre-invasive status and clinical relevance, as it is the earliest stage where patients typically present symptoms. Case 2 examined five breast tumor samples, including HER2-negative, ER-positive, PR-positive, and triple-negative subtypes. In three of these breast samples, the same epitope was identified and was synthesized by identical gene variants. This finding suggests the presence of shared antigenic targets across subtypes. We identified the top MHC class I and class II epitopes for both cancer types. In cervical carcinoma, the most immunogenic epitopes were found in proteins expressed by HSPG2 and MUC5AC. In breast carcinoma, epitopes with the highest potential were derived from proteins expressed by BRCA2 and AHNAK2. These epitopes were further validated through pMHC-TCR modeling analysis. Despite differences in cancer type and tumor subtype, both case studies successfully identified high-potential epitopes suitable for personalized vaccine design. The integration of AutoEpiCollect 2.0 streamlined the variant analysis workflow and reduced the time required to identify key tumor antigens. This study demonstrates the value of automated data integration in genomic analysis for cancer vaccine development. Furthermore, by applying RNAseq in a standardized workflow, the approach enables both patient-specific and population-level vaccine design, based on statistically frequent gene variants observed across tumor datasets. AutoEpiCollect 2.0 is freely available as a website based tool for user to design vaccine. Full article

Optical Wireless Communication (OWC) technologies are emerging as promising complements to radio-frequency systems, offering high bandwidth, spatial confinement, and license-free operation. Within this domain, Visible Light Communication (VLC) and Optical Camera Communication (OCC) represent two distinct paradigms with divergent performance and security profiles. While VLC leverages LED-photodiode links for high-speed data transfer, OCC exploits ubiquitous image sensors to decode modulated light patterns, enabling flexible but lower-rate communication. Despite their potential, both remain vulnerable to various attacks, including eavesdropping, jamming, spoofing, and privacy breaches. This work applies—and extends—the Risk Matrix (RM) methodology to systematically evaluate the security of VLC and OCC across reconnaissance, denial, and exploitation phases. Unlike prior literature, which treats VLC and OCC separately and under incompatible threat definitions, we introduce a unified, domain-specific risk framework that maps empirical channel behavior and attack feasibility into a common set of impact and likelihood indices. A normalized risk rank (NRR) is proposed to enable a direct, quantitative comparison of heterogeneous attacks and technologies under a shared reference scale. By quantifying risks for representative threats—including war driving, Denial of Service (DoS) attacks, preshared key cracking, and Evil Twin attacks—our analysis shows that neither VLC nor OCC is intrinsically more secure; rather, their vulnerabilities are context-dependent, shaped by physical constraints, receiver architectures, and deployment environments. VLC tends to concentrate confidentiality-driven exposure due to optical leakage paths, whereas OCC is more sensitive to availability-related degradation under adversarial load. Overall, the main contribution of this work is the first unified, standards-aligned, and empirically grounded risk-assessment framework capable of comparing VLC and OCC on a common security scale. The findings highlight the need for technology-aware security strategies in future OWC deployments and demonstrate how an adapted RM methodology can identify priority areas for mitigation, design, and resource allocation. Full article

Traditional calibration methods rely on precise targets and frequent manual intervention, making them time-consuming and unsuitable for large-scale deployment. Existing learning-based approaches, while automating the process, are typically limited to single LiDAR–camera pairs, resulting in poor scalability and high computational overhead. To address these limitations, we propose a lightweight calibration network with flexibility in the number of sensor pairs, making it capable of jointly calibrating multiple cameras and LiDARs in a single forward pass. Our method employs a frozen pre-trained Swin Transformer as a shared backbone to extract unified features from both RGB images and corresponding depth maps. Additionally, we introduce a cross-modal channel-wise attention module to enhance key feature alignment and suppress irrelevant noise. Moreover, to handle variations in viewpoint, we design a modular calibration head that independently estimates the extrinsics for each LiDAR–camera pair. Through large-scale experiments on the nuScenes dataset, we show that our model, requiring merely 78.79 M parameters, attains a mean translation error of 2.651 cm and a rotation error of $0.{246}^{\circ }$, achieving comparable performance to existing methods while significantly reducing the computational cost. Full article

With the rapid development of the Internet of Medical Things (IoMT), the data generated and collected by various sensors and medical devices are gradually increasing. How to realize flexible, efficient, and secure data sharing while ensuring data confidentiality and patient privacy has become a critical research challenge. The traditional Public Key Infrastructure (PKI) must deal with the complicated certificate management problem. An identity-based cryptosystem has the inherent key-escrow risk. These concerns make them unsuitable for resource-constrained and dynamic IoMT environments. To address it, this paper introduces a cloud data sharing protocol for IoMT using a Certificateless Proxy Re-encryption (CL-PRE) scheme that integrates an efficient access-list-based user revocation mechanism. In our system, a patient’s data can be encrypted and securely stored in a semi-trusted third party like the cloud server. When the patient wants to grant the access to designated users, e.g., doctors or medical institutions, a delegated proxy server will re-encrypt the ciphertext to a new one, which is decryptable by the designators. The proxy server also learns nothing during the re-encryption process, so as to maintain the end-to-end confidentiality. As for the security, the authors formally prove that the proposed CL-PRE mechanism for IoMT achieves Type-I and Type-II indistinguishability against adaptive chosen-identity and chosen-ciphertext attacks (IND-PrID-CCA) under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. Moreover, the functional and computational comparisons with previous studies reveal the qualitative advantage of simultaneously achieving certificateless properties and user revocation, and the quantitative advantage of an optimized encryption cost (requiring only one bilinear pairing and two scalar multiplications), making it a theoretically efficient solution for resource-constrained IoMT devices. Full article

This paper examines an approach to local-level community action for the global Sustainable Development Goals (SDGs), amid the growing importance of context-specific implementations to accelerate progress. Land-use governance is critical for contributions to the SDGs, as it shapes a wide range of environmental, social, and economic outcomes. Wildlife conservancies provide an innovative community-driven land-stewardship model that has proliferated across rangelands in various African countries as a sustainable development strategy. This study explores the potential contribution and capacity of conservancies, as a form of land-use governance, in advancing the SDGs at local levels. Using case studies from Kenya’s Maasai Mara, the research draws on qualitative primary data collected through in-depth interviews, a focus group discussion, observation, and document review, supplemented by secondary data obtained from a literature review. The data was analyzed thematically. The results show that conservancies address key socio-ecological challenges corresponding with multiple SDGs, particularly those related to poverty reduction, food security, climate action, and life on land. However, significant segments of local communities remain marginalized in decision making and benefit sharing, a situation rooted in pre-existing social hierarchies and weak governance institutions, raising concerns about social justice. Other major limitations are related to the conservancies’ over-reliance on tourism, and local people’s high dependence on natural resources. To resolve these limitations, the study recommends improving local governance via institutional strengthening, capacity building, gender empowerment, and stakeholder partnerships; diversifying income sources to reduce financial vulnerability; and adopting strategies to alleviate high dependence on natural resources in the long term. Full article

In recent years, remote data management environments have been increasingly deployed across diverse infrastructures, accompanied by a rapid surge in the demand for sharing and collaborative processing of sensitive data. Consequently, ensuring data security and privacy protection remains a fundamental challenge. A representative example of such an environment is the cloud, where efficient mechanisms for secure data sharing and access control are essential. In domains such as finance, healthcare, and public administration, where large volumes of sensitive information are processed by multiple participants, traditional access-control techniques often fail to satisfy the stringent security requirements. To address these limitations, Key-Aggregate Proxy Re-Encryption (KA-PRE) has emerged as a promising cryptographic primitive that simultaneously provides efficient key management and flexible authorization. However, existing KA-PRE constructions still suffer from several inherent security weaknesses, including aggregate-key leakage, ciphertext insertion and regeneration attacks, metadata exposure, and the lack of participant anonymity within the data-management framework. To overcome these limitations, this study systematically analyzes potential attack models in the KA-PRE setting and introduces a novel KA-PRE scheme designed to mitigate the identified vulnerabilities. Furthermore, through theoretical comparison with existing approaches and an evaluation of computational efficiency, the proposed scheme is shown to enhance security while maintaining practical performance and scalability. Full article

Background/objectives: Early literacy is widely promoted, yet its broader developmental benefits remain underexamined regarding key indicators of brain development. This study examines whether early literacy exposure in toddlerhood predicts motor skill development at the end of kindergarten. Methods: Participants comprised 1006 boys and 991 girls from the Quebec Longitudinal Study of Child Development (QLSCD) birth cohort. Early literacy stimulation was measured at age 2 years using parent reports of frequency of shared reading, looking at books or comics, and pre-writing activities such as scribbling and tracing. At age 6 years, child motor development was assessed by trained examiners. Sex-stratified multiple regression models were examined, adjusting for pre-existing and concurrent child and family characteristics. Results: Early literacy stimulation was significantly associated with better motor control skills among girls (β = 0.10, p < 0.05). For boys, a non-significant positive trend was observed for both motor and locomotion skills. Conclusions: Our findings underscore the lasting influence of early literacy stimulation and subsequent motor skills—particularly for girls who may receive less gross motor encouragement than boys. As such, promoting literacy-rich environments in toddlerhood is a family strategy to support healthy, confident, and active youth development. Full article

The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers, we explore an alternative, concept-first architecture in which spatial understanding emerges from asynchronous concept agents that directly instantiate and manage semantic entities. Our robot employs two spatial concepts—room and door—implemented as autonomous processes within a cognitive distributed architecture. These concept agents cooperatively build a shared scene graph representation of indoor layouts through active exploration and incremental validation. The key architectural principle is hierarchical constraint propagation: Room instantiation provides geometric and semantic priors to guide and support door detection within wall boundaries. The resulting structure is maintained by a complementary functional principle based on prediction-matching loops. This approach is designed to yield an actionable, human-interpretable spatial representation without relying on any pre-existing global metric map, supporting scalable operation and persistent, task-relevant understanding in structured indoor environments. Full article

Swarm-based UAV cooperative ground target detection faces critical challenges including sensitivity to small targets, susceptibility to occlusion, and data heterogeneity across distributed platforms. To address these issues, we propose FedGTD-UAVs—a privacy-preserving federated transfer learning (FTL) framework optimized for real-time swarm perception tasks. Our solution integrates three key innovations: (1) an FTL paradigm employing centralized pre-training on public datasets followed by federated fine-tuning of sparse parameter subsets—under severe non-Independent and Identically Distributed (non-IID) data distributions, this paradigm ensures data privacy while maintaining over 98% performance; (2) an Space-to-Depth Convolution (SPD-Conv) backbone that replaces lossy downsampling with lossless space-to-depth operations, preserving fine-grained spatial features critical for small targets; (3) a lightweight Global Context Network (GCNet) module leverages contextual reasoning to effectively capture long-range dependencies, thereby enhancing robustness against occluded objects while maintaining real-time inference at 217 FPS. Extensive validation on VisDrone2019 and CARPK benchmarks demonstrates state-of-the-art performance: 44.2% mAP@0.5 (surpassing YOLOv8s by 12.1%) with 3.2× superior accuracy-efficiency trade-off. Compared to traditional centralized learning methods that rely on global data sharing and pose privacy risks, as well as the significant performance degradation of standard federated learning under non-IID data, this framework successfully resolves the core conflict between data privacy protection and detection performance maintenance, providing a secure and efficient solution for real-world deployment in complex dynamic environments. Full article

The shadow economy weakens fiscal sustainability, hampers the financing of public goods, and impedes the achievement of sustainable development goals. The informal sector remains a persistent challenge for policymakers, as it distorts competition, reduces transparency, and undermines the effectiveness of economic and fiscal policies. The aim of this article is to identify the key factors determining the size of the shadow economy in European Union countries and to provide policy-relevant insights. The analysis covers data on the share of the informal economy in GDP and macroeconomic variables such as GDP per capita, consumer price index, average wages, household consumption, government expenditure, and unemployment, as well as indicators of digital development in society and the economy (DESI, IDT), the share of cashless transactions in GDP, and information on the implementation of digital tax administration tools and restrictions on cash payments. Five hypotheses (H1–H5) are formulated concerning the effects of income growth, labour market conditions, digitalisation, cashless payments, and tax administration tools on the shadow economy. The research question addresses which factors—macroeconomic conditions, economic and social digitalisation, payment structures, and fiscal innovations in tax administration—play the most significant role in determining the size of the shadow economy in EU countries and whether these mechanisms have broader implications for fiscal sustainability and sustainable development. The empirical strategy is based on multilevel models with countries as clusters, complemented by correlation and comparative analyses. The results indicate that the most significant factor in limiting the size of the shadow economy is the level of GDP per capita and its growth, whereas the impact of card payments appears to be superficial, reflecting overall increases in wealth. Higher wages, household consumption, and digital development as measured by the DESI also play an important role. The implementation of digital solutions in tax administration, such as SAF-T or e-PIT/pre-filled forms, along with restrictions on cash transactions, can serve as complementary measures. The findings suggest that sustainable strategies to reduce the shadow economy should combine long-term economic growth with digitalisation and improved tax administration, which may additionally foster the harmonisation of economic systems and support sustainable development. Full article

Background: Metabolic flexibility, the ability to efficiently switch between fuel sources in response to changing nutrient availability and energy demands, is recognized as a key determinant of metabolic health. In a recent randomized controlled human feeding trial, overweight individuals receiving American black elderberry juice (EBJ) demonstrated improvements in multiple clinical indices of metabolic flexibility, but the mechanisms of action were unexplored. The objective of this study was to utilize RNA sequencing to examine how EBJ modulates the transcriptional response to fasting and feeding, focusing on pathways related to metabolic flexibility. Methods: Overweight or obese adults (BMI > 25 kg/m²) without chronic illnesses were randomized to a 5-week crossover study protocol with two 1-week periods of twice-daily EBJ or placebo (PL) separated by a washout period. RNA sequencing was performed on peripheral blood mononuclear cells from 10 participants to assess transcriptomic responses collected at fasting (pre-meal) and postprandial (120 min post-meal) states during a meal-challenge test. Results: The fasted-to-fed transition for EBJ showed 234 differentially expressed genes following EBJ consumption compared to 59 genes following PL, with 44 genes shared between interventions. EBJ supplementation showed significantly higher enrichment of several metabolic pathways including insulin, FoxO, and PI3K–Akt signaling. KEGG pathway analysis showed 27 significant pathways related to metabolic flexibility compared to 7 for PL. Conclusions: Our findings indicate that short-term elderberry juice consumption may promote metabolic flexibility in overweight adults. Full article

Community detection is a fundamental task for understanding network structures, crucial for identifying groups of nodes with close connections. However, existing methods generally treat all connections in networks as equally important, overlooking the inherent inequality of connection strengths in social networks, and often require large quantities of labeled data. To address these challenges, we propose a few-shot community detection framework, Strong Triadic Closure Community Detection with Prompt (STC-CDP), which combines the Strong Triadic Closure (STC) principle, Graph Neural Networks, and prompt learning. The STC principle, derived from social network theory, states that if two nodes share strong connections with a third node, they are likely to be connected with each other. By incorporating STC constraints during the pre-training phase, STC-CDP can differentiate between strong and weak connections in networks, thereby more accurately capturing community structures. We design an innovative prompt learning mechanism that enables the model to extract key features from a small number of labeled communities and transfer them to the identification of unlabeled communities. Experiments on multiple real-world datasets demonstrate that STC-CDP significantly outperforms existing state-of-the-art methods under few-shot conditions, achieving higher F1 scores and Jaccard similarity particularly on Facebook, Amazon, and DBLP datasets. Our approach not only improves the precision of community detection but also provides new insights into understanding connection inequality in social networks. Full article

This study investigates how bank mergers and acquisitions (M&As) reshape the monitoring architecture of syndicated loans and, by extension, borrowers’ financing conditions. Using a global panel of 20,299 syndicated loan contracts, originating in 43 countries between 1982 and 2020, we link LPC DealScan data to Securities Data Company M&A records to trace each loan’s lead arrangers before and after consolidation events. Fixed-effects regressions, enriched with borrower- and loan-level controls, reveal three key patterns. First, post-merger loans exhibit significantly more concentrated syndicates: the Herfindahl–Hirschman Index rises by roughly 130 points and lead arrangers retain an additional 0.8–1.1 percentage points of the loan, consistent with heightened monitoring incentives. Second, these effects are amplified when information asymmetry is acute, i.e., for opaque or unrated firms, supporting moral hazard theory predictions that lenders internalize greater risk by holding larger stakes. Third, relational capital tempers the impact of consolidation: borrowers with repeated pre-merger relationships face smaller increases in syndicate concentration, while switchers experience the most significant jumps. Robustness checks using lead arranger market share, alternative spread measures, and lag structures confirm the findings. Overall, the results suggest that bank consolidation strengthens lead arrangers’ incentives to monitor but simultaneously reduces risk-sharing among participant lenders. For borrowers, the net effect is a trade-off between potentially tighter oversight and reduced syndicate diversification, with the balance hinging on transparency and prior ties to the lender. These insights refine our understanding of how structural shifts in the banking sector cascade into corporate credit markets and should inform both antitrust assessments and borrower funding strategies. Full article

With the rapid evolution of smart grids, secure and efficient data communication among hierarchical sensor devices has become critical to ensure privacy and system integrity. However, existing protocols often fail to balance security strength and resource constraints of terminal sensors. In this paper, we propose a novel identity-based secure data communication protocol tailored for hierarchical sensor groups in smart grid environments. The protocol integrates symmetric and asymmetric encryption to enable secure and efficient data sharing. To reduce computational overhead, a Bloom filter is employed for lightweight identity encoding, and a cloud-assisted pre-authentication mechanism is introduced to enhance access efficiency. Furthermore, we design a dynamic group key update scheme with minimal operations to maintain forward and backward security in evolving sensor networks. Security analysis proves that the protocol is resistant to replay and impersonation attacks, while experimental results demonstrate significant improvements in computational and communication efficiency compared to state-of-the-art methods—achieving reductions of 73.94% in authentication computation cost, 37.77% in encryption, and 55.75% in decryption, along with a 79.98% decrease in communication overhead during authentication. Full article