Noroviruses and enteroviruses are major causes of endemic gastrointestinal disease associated with substantial disease burden. However, viral gastroenteritis is often diagnosed based on symptoms, with etiology infrequently tested or reported, so little information exists on community-level transmission dynamics. In this study, we demonstrate that norovirus (NoV) genogroup II and enterovirus (EV) viral loads in wastewater reveal transmission dynamics of these viruses. We report NoV and EV concentrations in wastewater from 363 samples between December 5 2020 and October 10 2022 (sampled every second day). Virus concentrations in wastewater were low during 2021, and increased in 2022. Wastewater recapitulated periods of increased clinical cases, and also identified silent waves of transmission. We used the measured wastewater loads to estimate the effective reproductive number (R e). The R e for both NoV and EV peaked between 1.1 and 1.2. However, the usual seasonality of NoV transmission was upended by non-pharmaceutical interventions implemented to mitigate the COVID-19 pandemic, leading to correlated transmission dynamics of NoV GII and EV during 2021-2022. This highlights the use of wastewater to understand transmission dynamics of endemic enteric viruses and estimate relevant epidemiological parameters, including R e .

Ultra-Reliable Low-Latency Communication is a key feature of 5G, yet achieving its strict one-way latency target remains challenging in real-world deployments. While previous work proposes latency reduction techniques, most are theoretical or simulation-based and overlook practical bottlenecks in actual systems. In this paper, we analyze and optimize latency with open-source 5G RAN software. We characterize latency sources arising from 5G specifications and implementation-level factors, along with their complex interplays. Guided by this analysis, we introduce improvements reducing one way latency by 39.28 % in the downlink and 55.38 % in the uplink. Our results show the importance of system-level experimentation and provide a blueprint for advancing toward URLLC targets in both 5G and future cellular networks.

By interfacing computing logic directly to the DRAM banks, bank-level Compute-near-Memory (CnM) architectures promise to mitigate the bottleneck at the memory interconnect. While this computation paradigm heavily reduces the energy requirements for data movement across the system, current solutions fail to co-optimize hardware and software to further increase efficiency. Instead, in this manuscript we present SideDRAM, a co-designed bank-level CnM architecture to enable massively parallel and energy-efficient computations near DRAM. In contrast with past solutions, we support flexible data typing and heterogeneous quantization, relying on the robustness of workloads to employ small bitwidths, and enable a row-wide access to the banks to exploit parallelism and spatial locality. As a result, SideDRAM integrates (1) software-defined SIMD (SoftSIMD) datapaths, supporting low-energy computing with flexible precision, (2) an interface to the banks based on very wide registers (VWRs), enabling asymmetric data access to both utilize the full DRAM bank bandwidth and leverage data locality at the datapath, and (3) a low-overhead distributed control plane, allowing the efficient handling of variable data typing. We benchmark SideDRAM as a near-DRAM solution by analyzing the area, performance and energy consumption of an HBM2 CnM channel executing heterogeneously quantized machine learning models. The results show that, compared to the state-of-the-art FIMDRAM design, energy improvements of up to 67% are achieved when a DeiT-S inference is executed with a batch size of 16 under the same area constraints, resulting in energy-delay-area product (EDAP) savings that reach 83%. When comparing to a massively parallel mixed-signal CnM solution, SideDRAM consistently obtains similar performance and better energy efficiency results (geomean of 15x improvement across workloads) at a lower area overhead.

This paper revisits the topic of induced voltages and currents in underground cables, building on recent advancements in both cable coupling and soil modeling. The induced current in an underground cable is computed by solving the field-to-buried cable coupling equations in the frequency domain using Green’s functions. The results obtained reinforce the importance of including the ground admittance in addressing transient problems in underground cables, especially in poor conducting soils. Comparative analysis reveals that combining Sunde's ground-return impedance formulation with Vance's approximation for computing the ground-return admittance aligns closely with results obtained using the more complete formulation of Xue et al., offering a simplified yet accurate method for calculating induced currents and voltages in buried cables. Also, it is demonstrated that the frequency dependence of soil’s electrical parameters significantly im-pacts the induced current in the cable, leading to a decrease in its amplitude, notably in poor conducting soils. The findings of this study underline the importance of a detailed ground-return parameter computation and the inclusion of soil param-eter frequency dependence for the accurate assessment of induced effects in underground cables.

This paper proposes an extended RLC circuit model for grounding rods that incorporates the frequency-dependent nature of both the grounding input impedance and the soil electrical parameters. The accuracy of the model, in terms of its predictions for Ground Potential Rise (GPR) waveforms and impulse impedance values, is demonstrated through com-parisons with results obtained using an accurate electromagnetic model, assuming both first and subsequent return stroke current waveforms. The model is fully compatible with EMTP-type simulation programs, requiring only the grounding rod's geometry and the soil resistivity as input parameters. Given the characteristics of the proposed model— its demon-strated accuracy and EMTP compatibility—it can be a useful tool for modeling the typical grounding systems of distri-bution line poles in time-domain simulations of lightning-induced voltages.

Strained ring systems such as cyclobutanes offer unique opportunities for synthetic innovation. Its significant ring strain, caused by compressed bond angles, presents synthetic challenges; however, this same strain renders the ring a valuable handle for activation and subsequent functionalization for the construction of complex molecular structures that would otherwise be difficult to obtain through conventional methods. As such, the development of efficient strategies to construct and transform cyclobutane scaffolds remains a compelling area of research, with broad implications for the synthesis of complex, bioactive molecules. Heteroatoms play a major role in medicinal chemistry by modulating the physicochemical properties of drug candidates, including electronic properties, hydrogen bonding potential, and metabolic stability. In this context, heteroatom-substituted cyclobutanes are increasingly used in pharmaceuticals and agrochemicals, where they exhibit strong pharmacological potential. In particular, β-disubstituted cyclobutanes have gained considerable attention due to their conformational rigidity induced by the cyclobutane ring. Nevertheless, general synthetic access to heteroatom-substituted β-cyclobutanes remains largely underexplored and generally lacks efficiency, selectivity, and broad applicability. The first objective of this thesis was to establish a synthesis of novel donor-acceptor amino-cyclobutane monoesters, and to use these new compounds in catalytic, diastereoselective [4+2] annulation reaction with indoles as partners. The silylium-catalyzed methodology developed was applied both inter- and intramolecularly, with the latter yielding divergent alkaloid scaffolds depending on the reaction temperature. DFT studies rationalized this divergent outcome, and the synthetic utility of the method was demonstrated in the divergent synthesis of structurally diverse alkaloids, starting from simple and commercially available starting materials. The second part of this thesis focused on expanding the scope of available aminocyclobutane esters. The previously developed approach, relying on in situ dehydrobromination and subsequent Michael addition, to synthesize donor-acceptor aminocyclobutane monoesters was streamlined and expanded to a wide variety of relevant N-heterocycles, such as imidazoles, azoles, and nucleobase derivatives, allowing the elaboration of a new library of aminocyclobutane esters and amides.
The scope of this approach using other hetero-atom nucleophiles, such as sulfur nucleophiles, was then investigated. As a result, we developed the diastereoselective synthesis of thiocyclobutane esters and amides. Moreover, we achieved the enantioselective version of the sulfa-Michael addition onto cyclobutenes using a chiral cinchona squaramide organocatalyst, affording 1,2-substituted thiocyclobutanes with high enantioselectivity. Finally, attention was turned to the synthesis of multi-substituted aminocyclobutane esters. This led us to the development of the first [2+2] cycloaddition between ynamides and simple acrylates. The resulting stable cyclobutenes obtained were selectively hydrogenated to afford multi-substituted aminocyclobutane monoesters with two distinct stereoconfigurations, offering a practical and modular route to novel β-cyclobutane amino acid derivatives.

Quantum computing is expected to complement classical von-Neumann architectures in solving problems beyond the reach of conventional digital machines. This new model of computation leverages the rich complexity of many-body quantum systems to perform computations in a space that scales exponentially with the number of quantum bits without incurring an exponential cost in resources such as time, space, or energy. Over the past decade, remarkable experimental progress in quantum processing units has been made possible through the convergence of advances in information theory, quantum physics, materials science, and electrical engineering. Current research is actively benchmarking various platforms to implement scalable quantum architectures with high fidelity operation, aiming to integrate an increasing number of interconnected qubits on a single platform. This thesis investigates technological advancements in semiconductor spin qubits, with a focus on silicon-based quantum device architectures leveraging fully-depleted silicon-on-insulator (FD-SOI) technology. Emphasis is placed on quantum confinement, spin qubit design, and fabrication techniques that enable a compact device integration. Theoretical foundations are provided through an overview of quantum confinement in nanostructures and its application to single-electron transistors (SETs) and spin qubits. Key qubit types based on semiconductor quantum dots including Lossâ DiVincenzo, singletâ triplet, and exchange-only configurations are discussed. Electric-dipole spin resonance (EDSR) is presented as a method for operating single-qubit gates with high frequency and fidelity. Our experimental work explores advanced fabrication strategies for thin and ultra-thin SOI substrates, optimized for reproducibility and scalability. The successful realization of multi-gate FD-SOI SETs, demonstrating Coulomb oscillations at 4K and 10mK, confirms the compatibility of these devices with standard CMOS processes and supports the development of SETâ MOS hybrid circuits for both digital and analog applications. A novel back-gating approach, based on the so-called "Nanomole" fabrication process is introduced to achieve implantation-free dual-gate control at cryogenic temperatures. This method enhances electrostatic tunability and supports symmetric gating in FET devices at millikelvin temperatures. Simulations reveal that back-gate biasing offers in situ control over quantum dot shape and position, enabling tuning of key parameters such as volume inversion, valley splitting, and spinâ orbit coupling. Furthermore, the front-end-of-line (FEOL) integration of cobalt nanomagnets into spin qubit architectures is explored to enable fast, localized EDSR driving. Thin-film characterization and micromagnetic simulations support the design of three spin qubit architectures optimized for high and low magnetic field operation, featuring Co depletion gates, wrap-gates and side-horseshoe nanomagnets. While charge noise and contact resistance in ultra-thin SOI remain challenges for future efforts to benchmark qubit operation, proof-of-concept devices demonstrate the feasibility of FD-SOI-based quantum technologies with integrated ferromagnetic components. This work provides the experimental foundation for the development of scalable, CMOS-compatible quantum devices, highlighting innovative fabrication processes and architectures tailored for the monolithic integration of FD-SOI quantum and classical processing units.

Channel coding has become an indispensable component of modern communication systems. By introducing redundancy into the transmitted data, it enables the receiver to detect and correct errors without feedback, thereby significantly improving the efficiency of transceivers. As wireless standards continue to evolve, particularly toward 6G, channel coding faces ever greater demands on performance, flexibility, and implementation efficiency. However, the increasing complexity of advanced decoders, combined with the slowdown of Moore's law, has made it challenging to bridge the gap between coding theory and hardware implementation. Given that polar codes and low-density parity-check (LDPC) codes have been ratified as the 5G New Radio (NR) standard codes, the design of efficient decoders for these two modern codes has become a central bottleneck for 5G-NR and beyond.

In addition to support for varying block sizes, code rates, and code structures, 5G-NR imposes strict demands on polar codes and LDPC codes in terms of reliability, latency, and throughput. These challenges call for a cross-layer design perspective to tightly integrate algorithmic efficiency with architectural scalability. As a newly adopted class of codes in 5G, polar codes face challenges in low-latency and high-reliability of decoders. While node-based successive cancellation list (SCL) decoding has emerged as an effective approach to reduce the latency of SCL decoding, current node-based decoders are often constrained by their limited ability to generalize across diverse node types. This restricts their flexibility and leaves room for improvement in decoding speed. In this thesis, we propose a generalized node-based SCL decoder to minimize latency in single-frame polar decoding. Moreover, we introduce a frame-interleaving architecture to explore the throughput potential of polar decoders. For LDPC codes, the primary challenge in 5G-NR is to support a wide range of code configurations while meeting the required peak throughput. To this end, we develop a fully reconfigurable 5G-NR LDPC decoder architecture to fulfill these requirements. In summary, we present algorithmic and architectural optimizations for 5G-NR codes, along with two decoder implementations that are currently among the most efficient designs meeting the standard requirements.

Meanwhile, as 5G is making inroads to commercial devices, the global research community is actively exploring candidates for 6G channel coding. In contrast to 5G-NR, where code constructions are already fixed, the transition to 6G offers an opportunity to incorporate code design itself into the innovation process, prompting a revisit of coding schemes, decoding algorithms, and hardware architectures. In this thesis, we propose a new spatially-coupled LDPC code family called edge-spreading Raptor-like (ESRL) LDPC codes as a candidate for 6G next-generation mobile broadband. While preserving key features of 5G-NR standard codes, the proposed ESRL codes have advantages in error-rate performance, throughput, and hardware complexity compared to 5G-NR LDPC codes. To effectively realize the theoretical advantages in practice, we further present a fully reconfigurable high-throughput LDPC decoder implementation for ESRL codes. This ASIC can support a wide range of code rates and code lengths (up to five times longer than 5G) and achieve a high peak throughput of more than 100 Gbps, making it a promising solution for 6G wireless systems.

Heparin and heparan sulfate (HS) glycosaminoglycans (GAGs), essential components of the extracellular matrix (ECM), regulate a vast array of biological processes by modulating protein interactions and cellular signaling. Their structural complexity, driven by diverse sulfation patterns, underpins their broad physiological activities, including cell proliferation, differentiation, and neural development. However, the intrinsic heterogeneity of native HS complicates the elucidation of structure-activity relationships (SAR), hindering efforts to fully harness their therapeutic potential. This challenge underscores the need for structurally defined HS glycomimetics that can replicate the functional diversity of native HS with precise control and optimized therapeutic effects. 2 Here, we present a library of HS glycomimetics, rationally designed using molecular modeling and synthesized through a divergent synthesis strategy that allows sulfate groups to be installed at specific positions along the carbohydrate backbone. Biophysical characterizations unveil that these glycomimetics selectively bind and stabilize growth factors, including fibroblast growth factors (FGF-1, FGF-2) and nerve growth factor (NGF), in a sulfation-dependent manner without inducing anticoagulant activity, which is a critical prerequisite for successful clinical translation in nerve regeneration. The lead glycomimetic has neuritogenic ability because in two neuronal cell models, PC12 and SH-SY5Y, it enhances NGF-mediated neural maturation when immobilized on a surface. Moreover, functional studies in primary rat hippocampal neurons reveal that the lead glycomimetic potentiates FGF-2- mediated neurite outgrowth and spontaneous synaptic activity, effectively translating its molecular interactions into measurable cellular responses. By bridging molecular-level insights with functional bioactivity, this work establishes HS glycomimetics as precision tools for neurotrophic signaling. Their ability to fine-tune growth factor activity offers a versatile platform for regenerative medicine, extending beyond neural regeneration to broader tissue repair applications. These findings advance the development of next-generation carbohydrate-based therapeutics, unlocking new opportunities for precise and targeted regenerative strategies.

Halide perovskites have demonstrated remarkable efficiency in photovoltaics. However, their application is restricted by their limited operational stability. To address this, layered hybrid perovskites (LHPs) provided a more stable alternative. They incorporate hydrophobic organic spacers that template perovskite slabs. Despite improved stability, the commonly used organic cations are electrically insulating, leading to charge confinement within the inorganic layers that reduces the overall performance. To overcome this, it would be desirable to develop LHPs with enhanced functionalities by incorporating functional organic cations that respond to external stimuli, such as light, and improve their properties during device operation, which has been underexplored. This thesis focuses on developing novel LHPs with enhanced functionalities in response to light for application in photovoltaics. Chapter 1 introduces the background for LHPs, their functionalities, and potential applications. Chapter 2 focuses on the synthesis of the formamidinium (FA) based LHP model systems representing two architectures, Ruddlesden Popper (RP) phase incorporating benzyl ammonium (BNA) and Dion Jacobson (DJ) phase based on 1,4-phenylenedimethanammonium (PDMA) spacers. To advance their stability, compositional engineering by incorporating Cs+ into the perovskite framework was investigated in promoting higher-n phases, as obtaining n > 2 phases remains a challenge. While Cs improves photovoltaic performance, higher phases remain inaccessible due to the preferential formation of other low-dimensional structures. This sets the basis for further advancement of LHPs. In Chapter 3, we investigated novel LHPs incorporating aryl-acetylene-based spacers for RP and DJ phases, named (4-ethynylphenyl)methylammonium (BMAA) and buta-1,3-diyne-1,4-diylbis(4,1-phenylene)dimethylammonium (BDAA), respectively. Their distinctive optoelectronic and ionic properties were investigated, along with the propensity to photopolymerization. They were integrated into mixed-dimensional perovskite solar cells, which exhibited enhanced performance and improved operational stability. In Chapter 4, we expanded the approach by developing electroactive low-dimensional perovskites synthesized using napthalimide and napthalenediimide moieties. They were employed to modify or substitute traditional electron transport layers that pose critical stability issues, creating interfaces that facilitate charge transport. This improved photovoltaic performance and enhanced stability. Finally, in Chapter 5, we investigated light-responsive supramolecular strategies at the interface with charge-transport layers to suppress degradation of perovskite solar cells while maintaining high photovoltaic performance. This included functionalized triarylamine-based molecules, known for forming hole-transporting supramolecular stacks upon light exposure, and chiral P,M-(1-methylene-3-methyl-imidazolium)[6]helicenes relevant to chiral-induced spin selectivity (CISS). Their effects on structural and optoelectronic properties were examined, and their application in photovoltaics, demonstrating stability without sacrificing performance. As a result, this thesis contributed to developing a new generation of light-transforming LHPs with insights into their design, structural, and operational characteristics, revealing structure-property relationships toward advancing modern optoelectronics and photovoltaics.