Polycyclic aromatic dicarboximides (PADIs) as represented by naphthalene and perylene bis(dicarboximides) are useful molecules for a variety of functional materials and remain important synthetic targets for organic chemists. In this review, we summarize recent developments in the convergent synthetic approaches using palladium-catalysed annulation of peri-activated naphthalimides. Contrary to conventional linear synthetic methods where a single starting material is converted into a target molecule through a stepwise manner, the herein discussed convergent synthetic method enables rapid extension of the π-scaffold by combining multiple polyaromatic substrates into one. This method opened new possibilities for π-extended dicarboximides with so far up to five imide groups. Further exploration of annulation strategies may allow the preparation of previously inaccessible PADIs with fascinating molecular and material properties.

Digital labor platforms are reshaping the work landscape in Sub-Saharan Africa, promising enhanced productivity and empowerment. Yet, this study reveals a more complex reality, particularly in Rwanda, South Africa, and Zimbabwe. Based on 41 in-depth interviews, it exposes how algorithmic management systems deeply erode worker autonomy, highlighting significant financial, task, and behavioral dispossession. This research, grounded in neo-Marxist and postcolonial theories, scrutinizes the nuanced limitations of autonomy and the pervasive control exerted by algorithmic management, reflecting the lived experiences of workers. The findings illuminate enduring patterns of accumulation that echo historical exploitation, maintaining asymmetric power dynamics and dependence. Despite this, the study captures the agency of workers as they navigate and resist these systemic constraints, challenging the dominant techno-optimistic narrative. It underscores the critical need for contextually informed empirical research to shape policies that champion equity and elevate marginalized voices during transformative economic shifts.

Lithium niobate (LNO) and lithium tantalate (LTO) see widespread use in fundamental research and commercial technologies reaching from electronics over classical optics to integrated quantum communication. The mixed crystal system lithium niobate tantalate (LNT) allows for the dedicate engineering of material properties by combining the advantages of the two parental materials LNO and LTO. Vibrational spectroscopies such as Raman spectroscopy or (Fourier transform) infrared (IR) spectroscopy are vital techniques to provide detailed insight into the material properties, which is central to the analysis and optimization of devices. This work presents a joint experimental?theoretical approach allowing to unambiguously assign the spectral features in the LNT material family through both Raman and IR spectroscopy, as well as providing an in-depth explanation for the observed scattering efficiencies based on first-principles calculations. The phononic contribution to the static dielectric tensor is calculated from the experimental and theoretical data using the generalized Lyddane?Sachs?Teller relation and compared with the results of the first-principles calculations.

We propose a network-model realization of magnetic higher-order topological phases (HOTPs) in the presence of the combined space-time symmetry C4T -- the product of a fourfold rotation and time-reversal symmetry. We show that the system possesses two types of HOTPs. The first type, analogous to Floquet topology, generates a total of 8 corner modes at 0 or π eigenphase, while the second type, hidden behind a weak topological phase, yields a unique phase with 8 corner modes at ±π/2 eigenphase (after gapping out the counterpropagating edge states), arising from the product of particle-hole and phase rotation symmetry. By using a bulk Z4 topological index (Q), we found both HOTPs have Q=2, whereas Q=0 for the trivial and the conventional weak topological phase. Together with a Z2 topological index associated with the reflection matrix, we are able to fully distinguish all phases. Our work motivates further studies on magnetic topological phases and symmetry protected 2π/n boundary modes, as well as suggests that such phases may find their experimental realization in coupled-ring-resonator networks.

Motivated by the recent discovery of dielectric relaxation by quantum critical magnons in Cs2Cu2Mo3O12, we conduct a detailed analysis of its dielectric response and compare it to that in the isostructural compound Rb2Cu2Mo3O12. Measurements in the vicinity of the field-induced magnon softening show that its description in terms of 3D Bose-Einstein condensation of magnons quantum critical point is unaltered by the presence of dielectric relaxation. We also demonstrate the existence of dielectric relaxation anomalies at 19 K in Rb2Cu2Mo3O12 and discuss the implications for the microscopic origin of dielectric activity in two compounds.

Spontaneous symmetry breaking and more recently entanglement are two cornerstones of quantum matter. We introduce the notion of anisotropic entanglement ordered phases, where the spatial profile of spin-pseudospin entanglement spontaneously lowers the four-fold rotational symmetry of the underlying crystal to a two-fold one, while the charge density retains the full symmetry. The resulting phases, which we term entanglement smectic and entanglement stripe, exhibit a rich Goldstone mode spectrum and a set of phase transitions as a function of underlying anisotropies. We discuss experimental consequences of such anisotropic entanglement phases distinguishing them from more conventional charge or spin stripes. Our discussion of this interplay between entanglement and spontaneous symmetry breaking focuses on multicomponent quantum Hall systems realizing textured Wigner crystals, as may occur in graphene or possibly also in moiré systems, highlighting the rich landscape and properties of possible entanglement ordered phases.

A series of 1 : 1 co-crystals of decafluoroanthracene with naphthalene, anthracene, tetracene, pyrene, perylene, and triphenylene were grown and characterised using single-crystal X-ray diffraction at low and ambient temperature, (polarisation-dependent) fluorescence spectroscopy, and quantum mechanical computations. All of the molecular co-crystal structures are built from infinite stacks of alternating arene and decafluoroanthracene molecules representing the arene–perfluoroarene supramolecular synthon. Intermolecular interactions and packing features are discussed in detail. As a by-product, the heterodimer of tetracene and decafluoroanthracene was obtained after exposure to room light or under UV-irradiation of a solution of the monomers, and was structurally characterised. The fluorescence properties of the co-crystals of decafluoroanthracene with naphthalene, tetracene, pyrene, perylene, and triphenylene are similar to those of one of their components with only a small bathochromic or hypsochromic shift. This shows that the interaction between the components is weak and fits well into the picture of an arene–perfluoroarene interaction. An exception is 1 : 1 anthracene : decafluoroanthracene, which shows a bathochromic shift of both excitation and especially emission spectra of the co-crystal compared to those of its components, which indicates weak charge transfer coupling in the ground state followed by an exciplex-like emission.

Abstract The performance of thermoelectric materials is typically assessed using the dimensionless figure of merit, zT. Increasing zT is challenging due to the intricate relationships between electrical and thermal transport properties. This study focuses on Y2Te3-based thermoelectric materials, which are predicted to be promising for high-temperature applications due to their inherently low lattice thermal conductivity. A series of Y2+xTe3 compositions with excess Y is synthesized to explore the effects on electronic and structural characteristics. Density functional theory calculations suggest that additional Y atoms increase charge carriers, thereby enhancing electrical conductivity and boosting thermoelectric performance. X-ray diffraction analysis reveals that the presence of excess Y reduces lattice volume and alters bonding structures. Furthermore, the addition of Bi significantly enhances the power factor by promoting the segregation of elemental Bi particles and the formation of Y-Bi-rich grain boundaries, which improve weighted mobility. This microstructural optimization leads to a fourfold increase in the Seebeck coefficient, resulting in a peak zT of 1.23 at 973 K and a predicted maximum conversion efficiency of 10.3% under a temperature difference of 673 K. These findings highlight the potential of Y2Te3 for high-temperature thermoelectric applications and demonstrate the effectiveness of grain boundary engineering in enhancing thermoelectric performance.

We introduce a family of random matrices where correlations between matrix elements are induced via interaction-derived Boltzmann factors. Varying these yields access to different ensembles. We find a universal scaling behavior of the finite-size statistics characterized by a heavy-tailed eigenvalue distribution whose extremes are governed by the Fréchet extreme value distribution for the case corresponding to a ferromagnetic Ising transition. The introduction of a finite density of nonlocal interactions restores standard random-matrix behavior. Suitably rescaled average extremes, playing a physical role as an order parameter, can thus discriminate aspects of the interaction structure; they also yield further nonuniversal information. In particular, the link between maximum eigenvalues and order parameters offers a potential route to resolving long-standing problems in statistical physics, such as deriving the exact magnetization scaling function in the two-dimensional Ising model at criticality.

We study classical spin ice under uniaxial strain along the [111] crystallographic axis. Remarkably, such strain preserves the extensive ice degeneracy and the corresponding classical Coulomb phase. The emergent monopole excitations remain thermodynamically deconfined exactly as in the isotropic case. However, their motion under local heat bath dynamics depends qualitatively on the sign of the strain. In the low-temperature limit for negative strain, the monopoles diffuse, while for positive strain, they localize. Introducing additional ring exchange dynamics into the ice background transforms the localized monopoles into sub-dimensional excitations whose motion is restricted to diffusion in the (111)-plane. The phenomena we identify are experimentally accessible in rare-earth pyrochlores under uniaxial pressure as well as in tripod kagome materials. The diffusive versus localized nature of the monopoles manifests in characteristic magnetic noise spectra, which we compute.

We study the effect of bond disorder in extended Heisenberg-Kitaev models on the honeycomb lattice, relevant for materials such as α-RuCl3, in the semiclassical limit using a combination of T-matrix and real-space spin-wave approaches. Focusing on the regime of large applied magnetic field, we discuss two distinct but related disorder-induced phenomena, namely spin textures in the vicinity of isolated impurities and magnetic excitations below the bulk gap. A finite impurity concentration smears the field-tuned phase transition and turns the isolated in-gap states into impurity bands. As a result, there is a large field regime above the bulk transition into the high-field phase where impurity-induced states fill the bulk spin gap. We illustrate the field dependence of these in-gap states for parameters relevant for α-RuCl3, and we connect our results to heat-transport and NMR data which indicated their presence.

The non-Hermitian skin effect is a phenomenon in which an extensive number of states accumulates at the boundaries of a system. It has been associated to nontrivial topology, with nonzero bulk invariants predicting its appearance and its position in real space. Here, we demonstrate that the non-Hermitian skin effect has weaker bulk-edge correspondence than topological insulators: when translation symmetry is broken by a single non-Hermitian impurity, skin modes are depleted at the boundary and accumulate at the impurity site, without changing any bulk invariant. Similarly, a single non-Hermitian impurity may deplete the states from a region of Hermitian bulk.

Structural maintenance of chromosomes (SMC) protein complexes, including cohesin, condensin, and the Smc5/6 complex, are integral to various processes in chromosome biology. Despite their distinct roles, these complexes share two key properties: the ability to extrude DNA into large loop structures and the capacity to alter the superhelicity of the DNA double helix. In this review, we explore the influence of eukaryotic SMC complexes on DNA topology, debate its potential physiological function, and discuss new structural insights that may explain how these complexes mediate changes in DNA topology.

Topological superconductivity is a promising concept for generating fault-tolerant qubits. Early experimental studies looked at hybrid systems and doped intrinsic topological or superconducting materials at very low temperatures. However, higher critical temperatures are indispensable for technological exploitation. Recent angle-resolved photoemission spectroscopy results have revealed that superconductivity in the type-I Weyl semimetal—trigonal PtBi2 (t-PtBi2)—is located at the Fermi-arc surface states, which renders the material a potential candidate for intrinsic topological superconductivity. Here we show, using scanning tunnelling microscopy and spectroscopy, that t-PtBi2 presents surface superconductivity at elevated temperatures (5 K). The gap magnitude is elusive: it is spatially inhomogeneous and spans from 0 to 20 meV. In particular, the large gap value and the shape of the quasiparticle excitation spectrum resemble the phenomenology of high-Tc superconductors. To our knowledge, this is the largest superconducting gap so far measured in a topological material. Moreover, we show that the superconducting state at 5 K persists in magnetic fields up to 12 T.

The potential of wide-field magneto-optical Kerr microscopy for the characterisation of low-dimensional van-der-Waals crystals is explored using the example of Cr2Ge2Te6 flakes in the ten nanometers thickness range. Although the magnetic domains with an expected width in the hundred-nanometer range cannot be seen on this material due to the limited lateral resolution, we show that Kerr microscopy is nevertheless a very valuable method for measuring the magnetization loops on selectable thickness regions on the flake. From the loop character one can indirectly infer on the existence or suppression of band domains, which are the equilibrium patterns above a film thickness of about 7nm. We derived this characteristic thickness from the initial susceptibility of the hysteresis loops and used it to estimate the specific domain wall energy to be 2.7⋅10−4 J/m2. We further demonstrate a thickness- and light colour dependent sign inversion of the Kerr signal that is explained by a Fresnel-type depth sensitivity concept. Accordingly, the Kerr contrast is governed by the relative phase of the Kerr amplitude that can be freely adjusted by a rotatable compensator. The compensator is thus the decisive optical element in magneto-optical Kerr magnetometry and microscopy on low-dimensional materials. It needs to be appropriately aligned to avoid a cancelation of the Kerr contrast and to maximise the Kerr signal.

Entanglement properties of purified quantum states are of key interest for two reasons. First, in quantum information theory, minimally entangled purified states define the Entanglement of Purification as a fundamental measure for the complexity of the corresponding physical mixed state. Second, dynamical entanglement growth in purified states represents the main bottleneck for calculating dynamical physical properties on classical computers in the framework of tensor network states. Here, we demonstrate how geometric methods including parallel transport may be harnessed to reduce such dynamical entanglement growth, and to obtain a general prescription for maintaining (locally) optimal entanglement entropy when time-evolving a purified state. Adapting and extending by higher order skew corrections the notion of Uhlmann geometric phases, we reveal the relation between dynamical entanglement growth and the geometry of the Hilbert-Schmidt bundle as the mathematical foundation of purified states. With benchmarks on a non-integrable spin chain model, we compare the computational performance of matrix product state algorithms based on our present geometric disentangling method to previous approaches for taming entanglement growth in purified states. Our findings provide numerical evidence that geometric disentanglers are a powerful approach, superior in various aspects to known methods for disentangling purified states in a range of physically relevant computational scenarios. To exclude the effect of algorithmic imperfections, we also provide a numerically exact analysis for systems of moderate size.

Advancements in materials and technologies have enabled thin-film electronics to be directly developed on previously unsuitable substrates. This article explores the fabrication of two thin-film temperature sensors, thermistors, and resistance temperature detectors (RTDs), using stone-based substrates, including marble, brick, stone paper, and Limex paper. The thermistors and RTDs were fabricated utilizing Cu/InGaZnO and Zn, applying the sputtering deposition technique. The sensor’s performance was analyzed based on two different heating methodologies: one using a hotplate, and the other using localized heating from above. The sensors’ performance was characterized within a temperature range of \($25~^\circ $\) C– \($80~^\circ $\) C. While the marble thermistor demonstrated the highest sensitivity among all thermistors at −11.54% \($^\circ \text C^-1$\) , the stone paper RTD similarly showed the highest sensitivity among all RTDs at 0.064% \($^\circ \text C^-1$\) . The localized heating methodology on top of RTDs resulted in the stone paper and Limex showing negligible hysteresis. Moreover, the sensors demonstrated stable behavior in the multiple reliability tests. Furthermore, Zn-based RTDs were dissolvable in less than 36 h. The outcomes show that stone-based materials are promising natural eco-friendly substrates for temperature sensors leading to less hazardous electronic waste.

Et greci viri clarissimi qui historias collegeru[n]t: affirmaru[n]t. Una[m] ante iniciu[m] omniu[m] celi [et] terre cu[n]ctis simul [con]fusis forma[m] fuisse: postea disseparatis: diuisisq[ue] molibus cepisse mundu[m] hunc ordinem quem videmus. Vn[d] die kriechische[n] hocherlewchten man[n] durch die die hystorien vnd geschichte[n] versamelt worde sind. habe[n] auch veriehen das vor anbegynn[] aller ding des himels vnd der erde[n] dieweill noch yde ding bayaynander ware[n] ein eynige form gewesen sei: un[d] darnach nach absunderu[n]g vn[d] zertaylu[n]g der beyeina[n]der gewesen pürde. die werlt dise odnung vnd gestalt die wir sehen empfange[n] hab. And the highly enlightened Greeks who collected all the histories and accounts subscribed to the theory that before the beginning of all things of heaven and earth and while these things were still together, there was but a single form; that later through separation and division of matter, the world took on the order and structure in which we now see it.

In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.

Maritime transportation is vital for global trade, yet safety challenges persist, especially with increasing vessel density and narrow waterways. The standard Closest Point of Approach (CPA) method, used to estimate collision risk, has limitations due to its reliance on constant Course Over Ground (COG) and Speed Over Ground (SOG) assumptions, neglecting dynamic vessel behaviours, true ship dimensions, and environmental influences. Addressing these limitations, this paper introduces an enhanced CPA (eCPA) method that counters these deficits by incorporating ship trajectory predictions, probabilistic risk assessment, and real-time data integration. The eCPA method includes user- adjustable tools for trajectory interpolation, prediction, and anomaly elimination, making it adaptable to various maritime scenarios. Extensive testing in encounter scenarios demonstrates the effectiveness of our approach in improving maritime safety through robust and probabilistic estimation of encounter points. This method aligns with the Self-Integration and Self-Organising Systems (SISSY) initiative, exhibiting how autonomous maritime operations can benefit from self-improving and self-integrating systems to enhance navigation safety and efficiency.

Classical contrastive learning paradigms rely on manual augmentations like cropping, masking, dropping, or adding noise randomly to create divergent sample views from original data. However, the choice of which method to manipulate samples is often subjective and may destroy the latent pattern of the sample. In response, this paper introduces a novel contrastive learning paradigm without choosing sample view augmentation methods, termed Heterogeneous Spatial-Temporal Representation Contrasting (HSTRC). Instead of sample view augmentation, we employ dual branches with a heterogeneous spatial-temporal flipped structure to extract two distinct hidden feature views from the same source data, which avoids disturbing the original time series. Leveraging a combination of cross-branch spatial-temporal contrastive and projected feature contrastive loss functions, HSTRC can effectively extract robust representations from unlabeled time series data. Remarkably, by only fine-tuning the fully connected layers on top of extracted representations by HSTRC, we achieve the best performance across several Remaining Useful Life prediction datasets, showing up to 19.2% improvements over the state-of-the-art supervised learning methods and classical contrastive learning paradigms. Besides, further intensive experiments demonstrate HSTRC's effectiveness in active learning, out-of-distribution testing, and transfer learning scenarios.

Organic and inorganic thin-film electronics on flexible substrates offer a platform to develop lightweight wearable devices and circuits capable of conforming to unconventional surfaces. However, their performance, such as their frequency response and limited circuit complexity, still limits their applications. In this context, flexible InGaZnO-based thin-film transistors (TFTs) have been widely explored as they combine mechanical flexibility with comparably good electrical performance. This work presents the use of a single InGaZnO double gate TFT able to modulate the amplitude and mix the frequencies of AC signals with frequencies up to \($\approx 10\textkHz$\). When used as a modulator, the design with two independent asymmetric gates allows the simultaneous application of a high-frequency carrier and a low-frequency modulation signal. Operated at a supply voltage of 5 V, the device shows a modulation index up to 75%, which varies \($< 15\%$\) when the TFT is bent to a radius of 5 mm. This suggests the possibility of such configurations being used as simple low-frequency transmitters.

The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications.The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.

The worst-case runtime complexity to simulate identity-by-descent segments is quadratic in sample size. We propose two main techniques to reduce the compute time, which are motivated by coalescent and recombination processes. We observe average runtimes to simulate detectable IBD segments around a locus that scale approximately linearly in sample size and take a couple of seconds for sample sizes less than ten thousand. In contrast, we find that existing methods to simulate IBD segments take minutes to hours for sample sizes exceeding a few thousand. When using IBD segments to study recent positive selection around a locus, our efficient algorithm makes feasible statistical inferences that would be otherwise intractable.Competing Interest StatementThe authors have declared no competing interest.

Malaria genomic surveillance often estimates parasite genetic relatedness using metrics such as Identity-By-Decent (IBD), yet strong positive selection stemming from antimalarial drug resistance or other interventions may bias IBD-based estimates. In this study, we use simulations, a true IBD inference algorithm, and empirical data sets from different malaria transmission settings to investigate the extent of this bias and explore potential correction strategies. We analyze whole genome sequence data generated from 640 new and 3089 publicly available Plasmodium falciparum clinical isolates. We demonstrate that positive selection distorts IBD distributions, leading to underestimated effective population size and blurred population structure. Additionally, we discover that the removal of IBD peak regions partially restores the accuracy of IBD-based inferences, with this effect contingent on the population's background genetic relatedness and extent of inbreeding. Consequently, we advocate for selection correction for parasite populations undergoing strong, recent positive selection, particularly in high malaria transmission settings.

This paper investigates the threshold voltage instability in flexible n-type thin film transistors (TFTs) featuring a bottom-gate \($\textTi/\textAl_2\mathrmO_3/\textIGZO$\) MOS structure. Hysteresis, positive gate bias stress and negative gate bias recovery measurements were performed at room temperature on a set of 20 devices with the same channel width (W = 280 µm) and different channel length \($L$\) ranging from 100 µm down to 5 uµm. Both hysteresis (applying \($V_Gs$\) from -1 V to 5 V and vice versa) and stress (applying \($V_Gs=5\mathrmV$\) for 1000 s) data show a positive threshold voltage shift, which can be attributed to negative charge trapping at the channel/oxide interface and/or in the bulk oxide. Moreover, the trapping rate parameter exhibits a general decreasing trend over time with values ranging from 0.28 to 0.46 in the initial stress phase and a reduction up to 88% after 1000 \($s$\) of stress. Measurements also show that in most devices the stress-induced shift can be fully recovered by applying a \($VGs=-2\mathrmV$\) for 1000 s as a result of releasing charges previously trapped during stress.

Indium gallium zinc oxide (IGZO)/silver/IGZO (IAgI) electrodes were sputtered on films of tattoo-paper (TP), cellulose nanocrystals (CNCs), silk fibroin (SF), and on a 75:25 v/v % SF :CNCs blend with a view to developing biocompatible, sustainable materials for printed electronics. Drop-cast films of CNCs, SF and their blends show an average visible transmittance of 90.9%, 81.5%, and 49.5% respectively. Mechanical measurements reveal average strain at break values of 26% for CNCs, 13% for SF, and 5.4% for the blend, with respective Young's moduli of 1200 MPa, 270 MPa, and 1000 MPa. IAgI electrodes on glass (as controls) display an average visible transparency ~71.6%, a sheet resistance of 5 Ohm/sq, and a work function of ~ 4.4 eV. We transferred electrodes on CNCs, blend, and TP flexible substrates to a Kalanchoe blossfeldiana plant leaf and monitored their resistance over a full day/night cycle in a controlled environment as a proof-of-concept demonstration for sensing applications.

Integrating Abstract-Integrating interactive interfaces into everyday items is crucial for connecting the digital and physical worlds. However, many current interface technologies are obtrusive and difficult to integrate into the environment. This work presents eStone, an approach where natural stone surfaces are used as substrates for capacitive thin-film sensors acting as interactive interfaces. metallic thin films such as 200 nm-thick aluminum (AI) were sputter deposited and structured on a range of different stones, including marble, granite, porphyry, and limestone, creating conductive patterns and enabling the realization of capacitive sensors. The nanometer-scale thickness of these layers preserves the stone's natural appearance and texture and transforms them into functionalized and durable capacitive sensors. Combined with through-hole contacting vias and customized conventional readout electronics, e Stone demonstrates excellent performance when used as a capacitive controller to adjust the volume of a loudspeaker or the temperature of a thermostat.

In this work, we report on the design, fabrication, and characterization of dry electrodes for the measurement of pulse plethysmography signals from distal arteries via electrical impedance. Four finger-track electrode designs were fabricated using a screen-printing technique, employing a stretchable Ag paste on a TPU stretchable substrate transferred to a textile structure. Electromechanical measurements (i.e. resistance (Ω) versus elongation (mm)) showed the confirmation of three distinct stages: stretchability, relaxation, and swelling on the printed tracks, where the finger tracks #1, #2, and #4 demonstrated a stable return to zero resistance and track #3 exhibited a negative return-to-zero value, with a decrease of a normalized resistance of 0.5 Ω. Furthermore, a preliminary test with the electrodes, demonstrates plethysmography signal waveforms are comparable to typical waveforms obtained with disposable gel electrodes.

We report the fabrication of thermistors and thin-film transistors (TFTs), the crucial components and building blocks of sensor systems, on a transparent and flexible 100 μm-thin glass substrate. The thermal response of thermistors based on InGaZnO (IGZO) with Cu contacts exhibits an expected negative temperature coefficient of resistance of \($\approx 1\%^\circ\mathrmC^-1$\) at temperatures between 25°C and 60°C. IGZO TFTs were realized with channel lengths as short as 6 μm, displaying a high on-off current ratio of \($\approx 10^9$\) and an effective mobility of \($7.3 \textcm^2 \mathrmV^-1 \mathrms^-1$\). The AC performance of IGZO TFTs on flexible thin glass was also investigated for the first time resulting in a maximum oscillation frequency and transit frequency of up to 30.6 MHz and 10.4 MHz, respectively.

The management of electronics waste and the development of sustainable end-of-life strategies are key aspects of the green evolution of the electronics industry. To address this global issue, we implemented thin-film resistance temperature detectors (RTDs) using green sensing metals, such as Mg, Mo, and Zn, and poly-ether ether ketone (PEEK), as a biocompatible, flexible, and thermally resistant substrate. The environmentally friendly RTDs were characterized in a range of temperature, from 25 °C to 70 °C, showing consistent response and average sensitivities of \($1.1\times 10^-1$\) %/°C, \($7\times 10^-2$\) %/°C, and \($5.8\times 10^-2$\) %/°C for Mg, Mo, and Zn, respectively. At a constant temperature 25 °C, the effect of humidity variation from 10% to 90% on the resistance of the sensors was observed to be \($2.0\times 10^-5$\) %/relative humidity (RH), \($3.4\times 10^-2$\) %/RH, and \($5\times 10^-3$\) %/RH, respectively, for Mg, Mo, and Zn RTDs. Furthermore, the sensor’s response to mechanical strain was evaluated by bending the devices down to a 10-mm bending radius. In addition, the dissolution of the green RTDs in water allows the reusability of the substrate for a new fabrication batch, minimizing the amount of electronics waste generated. Through this study, a promising solution to environmental concerns, realizing is endowed for realizing temperature sensors, with applications in green and sustainable wearable systems is demonstrated.

Magnetically guided untethered devices are used in a variety of medical applications. These devices are typically powered by onboard battery units. Hydrogen fuel cells (FC) are a promising alternative power source for such small-scale devices since they rely on a sustainable fuels which produce electric power from the redox reaction of hydrogen and oxygen across a proton exchange membrane (PEM). Understanding the impact of decreasing the active electrode area in FCs is crucial for deploying FCs in untethered devices and gaining insights into the challenges of downscaling the devices. This paper investigates the performance of PEM FCs (PEMFCs) when their active area is reduced, and when the FC is supplied with reactants at different flow rates from a PEM electrolyzer. PEMFCs with three active electrode areas, 3.5 × 3.5 cm2, 2.7×2.7 cm2, and 1.6×1.6 cm2were designed, fabricated, and characterised. Maximum fuel cell output powers of 0.3 W, 0.09 W, and 0.03 W (maximum power densities of 0.025 W/cm2, 0.012 W/cm2, and 0.013 W/cm2) corresponding to the three aforementioned areas were achieved. Mathematical modeling of the PEMFC simulated the FC response, providing insights into the activation kinetics of the fuel cell. The smallest PEMFC with an active area of 1.6 × 1.6 cm2was used to power an inductor coil (rated 130 mA, 150 mH, 8 Ω). This study can guide the development of FCs to power untethered devices.

Flexible electronics have gained significant attention, thanks to the implementation of innovative systems in attractive fields of application, such as wearables, implants, and smart agriculture. However, the electronic and plastic waste resulting from such devices at the end of their life cycle can be hazardous for the environment. Natural, non-toxic, and recyclable electronic materials are needed to resolve this global issue. Here, we present the use of beeswax as sustainable and green substrate for thin-film electronics. Temperature sensors are fabricated on a temporary substrate, transferred on the natural beeswax carrier and encapsulated for consistent response. The calculated sensitivity of sensor is 0.18\\($\$\)backslash%backslash,^backslashcirc backslash,backslashhbox \C\^\-1\\\($\$\)in a temperature range from 10 to 25 textdegreeC, and the encapsulated sensors have shown reliable functionality up to 18 days. These results demonstrate the successful employment of this nature-derived material as green substrate and its integration with thin-film devices, representing a leap forward in the realization of natural and sustainable electronics with low-carbon footprint.

This study explores eco-friendly alternatives to flexible plastic substrates aiming to mitigate the environmental impact of electronics. Thin-film temperature sensors were fabricated on CRUSH FSCTN-certified paper substrates made with coconut, coffee, cacao, and cherry fiber waste, substituting 15% of traditional tree cellulose. The sensors were created by sputtering a semiconducting thin-film layer of InGaZnO and zinc electrodes onto the paper substrates. The devices' responses to relative humidity changes from 2.9 to 73% were recorded, alongside their sensing performance for temperatures \\($\$\)25 backslash, ^\backslashcirc\\($\$\)C and \\($\$\)70 backslash, ^\backslashcirc\\($\$\)C. The results indicate that the sensors perform optimally when subjected to sudden and significant temperature changes and portray sensitivities values of \\($\$\)-\\($\$\)1.83\\($\$\)backslash%textasciitilde backslash, ^backslashcirc \backslashtextrm\C\^\\backslashtext\-\\\\($\$\)\\($\$\)^1\\($\$\)(heating) and \\($\$\)-\\($\$\)1.38\\($\$\)\backslash\%\\($\$\) \\($\$\)backslash, ^\backslashcirc\\($\$\)\\($\$\)backslashhbox \C\^\\backslashtext\-\\\\($\$\)\\($\$\)^1\\($\$\)(cooling). Additionally, the dissolution of the transient sensors in water was examined to assess their sustainability.

Bislang ist die AfD noch nicht als organisierende Kraft in den Betrieben aufgefallen. Allerdings agieren dort immer mehr rechtsextreme Aktivist*innen klandestin.

The rapid growth of blockchain technology hasintroduced newsecurity challengesin decentralized systems.In this paper, we present Block-AD, a Blockchain Attack-Defense Capture The Flag (CTF) competition aimed at improving smart contract security skills and addressing evolving security threats in Next-Web3 environments. Unlike traditional CTF competitions, participants engage in attack and defense actions to exploit vulnerabilities and protect their smart contract services. This game-based approach concludes with rewards distributed from the competition’s vault and the allocation of non-fungible tokens (NFTs) as proof of participation. This approach aims to provide a fun and engaging way to improve the security skills of all participants, as well as to identify and fix vulnerabilities in smart contracts.

Abstract Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the ‘spectral species’ concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Ubiquitous reinforced concrete water storage tanks are quite popular and widely used in Palestine as in elsewhere in the world; they form pivotal components of major bulk-water carrier systems. In essence, they form lifelines to many communities; the water storage concept is as old as civilization itself. Location and land availability often dictate the topology of the tank’s structure. They may be either shallow and stubby or deep yet slender or anywhere in between having an aspect ratio dictated by overall site conditions. In all cases adequate structural analysis is mandatory.

Abstract The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression-based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression-like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that are associated with characteristic difficulties to express and interpret nonverbal behavior, such as social gaze behavior. The state of the art in diagnosis is the clinical interview that is time intensive for the clinicians and does not take into account any objective measures of behavior. We herewith propose an empirical approach that can potentially support diagnosis based on the assessment of nonverbal behavior in avatar-mediated interactions in virtual environments. In a first study, ASD individuals and a typically developed control group were interacting in dyads. Head motion, and eye gaze of both interlocutors were recorded, replicated to the avatars and displayed to the partner through a distributed virtual environment. The nonverbal behavior of both interaction partners was recorded, and resulting preprocessed data was classified with up to 92.9parcent classification accuracy, with the amount of eye area focus and the average horizontal gaze change being the most relevant features. We expect that such systems could improve the diagnostic assessment on the basis of objective measures of nonverbal behavior.

Most of the existing steady state detection approaches are designed for univariate signals. For multivariate signals, the univariate approach is often applied to each process variable and the system is claimed to be steady once all signals are steady, which is computationally inefficient and also not accurate. The article proposes an efficient online method for multivariate steady state detection. It estimates the covariance matrices using two different approaches, namely, the mean- squared-deviation and mean-squared-successive-difference. To avoid the usage of a moving window, the process means and the two covariance matrices are calculated recursively through exponentially weighted moving average. A likelihood ratio test is developed to compare the difference of the two covariance matrices and to detect the steady state. The intensive numerical studies and real case study show that the proposed method can accurately detect the steady state of a multivariate system.

Most of the peers accessing the services are under the assumption that the service accessed in a P2P network is utmost secured. By means of prevailing hard security mechanisms, security goals like authentication, authorization, privacy, non repudiation of services and other hard security issues are resolved. But these mechanisms fail to provide soft security. An exhaustive survey of existing trust and reputation models in P2P network regarding service provisioning is presented and challenges are listed. Trust issues like trust bootstrapping, trust evidence procurement, trust assessment, trust interaction outcome evaluation and other trust based classification of peer’s behavior into trusted,, inconsistent, un trusted, malicious, betraying, redemptive are discussed.

Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.