Regression modelling strategies for projected and sustainable kraft pulping of wheat straw.

The demand for paper and paper-based packaging has seen a massive increase in past years, resulting in accelerated deforestation to meet the rising demand, negatively impacting the environment, and there is a need to look towards different non-woody raw materials. Kraft pulping (KP) is widely used in paper making, for which the chemical dose, temperature, time, and energy required must be optimized, for which many insignificant experimental trials are performed. An effort is made to solve this problem by developing the regression equations with the help of Excel using One Factor at a Time Analysis (OFAT), followed by carrying out design of experiments (DoE) using orthogonal approach and regression analysis in Minitab software. Life cycle Assessment (LCA) using the Open-LCA software estimates the effect of chemicals and energy required during pulping on human health, ecosystem quality, and resource depletion. Using regression analysis, the equations for predicting kappa number, yield (%), total energy consumed, and mechanical properties of the paper sheet showed a good fit with an R2 value in the range of 0.90-0.99. Apart from that, the mechanical properties, namely tensile index (41.43 Nm/g), tear index (6.96 mN m2/g), bending stiffness (0.5 mN m), and burst index (3.92 kPa m2/g) of the unbeaten sheet, were determined experimentally at optimized conditions. Based on the Open-LCA result, the optimized pulping conditions had less impact on human health, ecosystem quality, and resource depletion. Industries can use the model to predict the values of kappa number, yield, mechanical properties, and energy consumption without performing optimization experiments that may impact the industry's economy to a greater extent.

CoInNet: A Convolution-Involution Network With a Novel Statistical Attention for Automatic Polyp Segmentation.

Polyps are very common abnormalities in human gastrointestinal regions. Their early diagnosis may help in reducing the risk of colorectal cancer. Vision-based computer-aided diagnostic systems automatically identify polyp regions to assist surgeons in their removal. Due to their varying shape, color, size, texture, and unclear boundaries, polyp segmentation in images is a challenging problem. Existing deep learning segmentation models mostly rely on convolutional neural networks that have certain limitations in learning the diversity in visual patterns at different spatial locations. Further, they fail to capture inter-feature dependencies. Vision transformer models have also been deployed for polyp segmentation due to their powerful global feature extraction capabilities. But they too are supplemented by convolution layers for learning contextual local information. In the present paper, a polyp segmentation model CoInNet is proposed with a novel feature extraction mechanism that leverages the strengths of convolution and involution operations and learns to highlight polyp regions in images by considering the relationship between different feature maps through a statistical feature attention unit. To further aid the network in learning polyp boundaries, an anomaly boundary approximation module is introduced that uses recursively fed feature fusion to refine segmentation results. It is indeed remarkable that even tiny-sized polyps with only 0.01% of an image area can be precisely segmented by CoInNet. It is crucial for clinical applications, as small polyps can be easily overlooked even in the manual examination due to the voluminous size of wireless capsule endoscopy videos. CoInNet outperforms thirteen state-of-the-art methods on five benchmark polyp segmentation datasets.

Treating Parvovirus Triggered Refractory Hemolytic Anemia with Rituximab in Renal Transplant Recipients - A Report of Two Cases.

Parvovirus B19 is a small (26 nm), nonenveloped, single-stranded DNA (5.6-kb) virus. The only known host for parvovirus B19 is humans. Parvovirus B19 is directly cytotoxic to erythroid precursor cells of the colony- and burst-forming units. Human parvovirus B19 is the etiologic agent of erythema infectiosum and chronic pure red cell aplasia in immunocompromised individuals. Acute parvovirus B19 infection should be suspected in immunocompromised patients, who present with reticulocytopenic hemolytic anemia and thrombocytopenia. Intravenous immunoglobulin (IVIg) is the standard treatment for parvovirus-induced cytopenias. We report two cases of postrenal transplant who presented with reticulocytopenic anemia and were found to have parvovirus infection. They did not respond to conventional treatment with intravenous gamma globulin. Both patients were treated with rituximab with which they had improvement in clinical and hematological parameters. There was no previous documentation of using rituximab in the treatment of parvovirus-triggered autoimmune hemolytic anemia postrenal transplant patients. This article illustrates how rituximab will be helpful in this setting, of course, it is a new thought but requires further studies and validation.

Integrable quantum many-body sensors for AC field sensing.

Quantum sensing is inevitably an elegant example of the supremacy of quantum technologies over their classical counterparts. One of the desired endeavors of quantum metrology is AC field sensing. Here, by means of analytical and numerical analysis, we show that integrable many-body systems can be exploited efficiently for detecting the amplitude of an AC field. Unlike the conventional strategies in using the ground states in critical many-body probes for parameter estimation, we only consider partial access to a subsystem. Due to the periodicity of the dynamics, any local block of the system saturates to a steady state which allows achieving sensing precision well beyond the classical limit, almost reaching the Heisenberg bound. We associate the enhanced quantum precision to closing of the Floquet gap, resembling the features of quantum sensing in the ground state of critical systems. We show that the proposed protocol can also be realized in near-term quantum simulators, e.g. ion-traps, with a limited number of qubits. We show that in such systems a simple block magnetization measurement and a Bayesian inference estimator can achieve very high precision AC field sensing.

Driving Enhanced Quantum Sensing in Partially Accessible Many-Body Systems.

The ground-state criticality of many-body systems is a resource for quantum-enhanced sensing, namely, the Heisenberg precision limit, provided that one has access to the whole system. We show that, for partial accessibility, the sensing capabilities of a block of spins in the ground state reduces to the sub-Heisenberg limit. To compensate for this, we drive the Hamiltonian periodically and use a local steady state for quantum sensing. Remarkably, the steady-state sensing shows a significant enhancement in precision compared to the ground state and even achieves super-Heisenberg scaling for low frequencies. The origin of this precision enhancement is related to the closing of the Floquet quasienergy gap. It is in close correspondence with the vanishing of the energy gap at criticality for ground-state sensing with global accessibility. The proposal is general to all the integrable models and can be implemented on existing quantum devices.

Global Sensing and Its Impact for Quantum Many-Body Probes with Criticality.

Quantum sensing is one of the key areas that exemplify the superiority of quantum technologies. Nonetheless, most quantum sensing protocols operate efficiently only when the unknown parameters vary within a very narrow region, i.e., local sensing. Here, we provide a systematic formulation for quantifying the precision of a probe for multiparameter global sensing when there is no prior information about the parameters. In many-body probes, in which extra tunable parameters exist, our protocol can tune the performance for harnessing the quantum criticality over arbitrarily large sensing intervals. For the single-parameter sensing, our protocol optimizes a control field such that an Ising probe is tuned to always operate around its criticality. This significantly enhances the performance of the probe even when the interval of interest is so large that the precision is bounded by the standard limit. For the multiparameter case, our protocol optimizes the control fields such that the probe operates at the most efficient point along its critical line. Finally, it is shown that even a simple magnetization measurement significantly benefits from our global sensing protocol.

Benford's law gives better scaling exponents in phase transitions of quantum XY models.

Benford's law is an empirical law predicting the distribution of the first significant digits of numbers obtained from natural phenomena and mathematical tables. It has been found to be applicable for numbers coming from a plethora of sources, varying from seismographic, biological, financial, to astronomical. We apply this law to analyze the data obtained from physical many-body systems described by the one-dimensional anisotropic quantum XY models in a transverse magnetic field. We detect the zero-temperature quantum phase transition and find that our method gives better finite-size scaling exponents for the critical point than many other known scaling exponents using measurable quantities like magnetization, entanglement, and quantum discord. We extend our analysis to the same system but at finite temperature and find that it also detects the finite-temperature phase transition in the model. Moreover, we compare the Benford distribution analysis with the same obtained from the uniform and Poisson distributions. The analysis is furthermore important in that the high-precision detection of the cooperative physical phenomena is possible even from low-precision experimental data.