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Soil erosion is a serious threat to agriculture and environmental sustainability in the risk‐prone rainfed Himalayan ecosystem. Hence, runoff and soil loss mitigation ability of four maize‐based intensified systems; maize (Zea mays) + turmeric (Curcuma longa), maize + ginger (Zingiber officinale), maize + colocasia (Colocasia esculenta), and maize + sweet potato (Ipomoea batatas) were tested against the sole maize. The study recorded 41 erosive events during the 2020–2022 monsoon season. A principal component analysis (PCA) was performed to transform correlated rainfall parameters into orthogonal principal components. A two‐dimensional biplot analysis examined the relationship between PCs, rainfall events, and rainfall parameters. The rainfall events were divided into three regimes based on cluster analysis using PC. Rainfall regime 1 was characterized by low rainfall, I30, rainfall erosivity (E), and moderate rainfall duration (D), regime2 with moderate rainfall, short duration, high I30, and E, and regime3 with high D, I30, and rainfall erosivity. The soil loss of regime‐3 was 9.29 and 3.24 times higher than regime‐1 and regime‐2, respectively. Cover crops reduced runoff by 21.5%–69.3% and soil loss by 54.1%–77.0% over control. Redundancy analysis (RDA) showed that rainfall parameters (I30, I60, D, and E) had a significant direct influence across the systems, explaining 37.7%–54.9% of response variable variance, with E being the most influential (47.4%–54.9%), indicating varied impacts on runoff and soil loss. The sweet potato was best suited with maize as an intercrop to minimize soil erosion and maximize the system's profitability. [ABSTRACT FROM AUTHOR]

Information processing typically occurs via the composition of modular units, such as the universal logic gates found in discrete computation circuits. The benefit of modular information processing, in contrast to globally integrated information processing, is that complex computations are more easily and flexibly implemented via a series of simpler, localized information processing operations that only control and change local degrees of freedom. We show that, despite these benefits, there are unavoidable thermodynamic costs to modularity - costs that arise directly from the operation of localized processing and that go beyond Landauer's bound on the work required to erase information. Localized operations are unable to leverage global correlations, which are a thermodynamic fuel. We quantify the minimum irretrievable dissipation of modular computations in terms of the difference between the change in global nonequilibrium free energy, which captures these global correlations, and the local (marginal) change in nonequilibrium free energy, which bounds modular work production. This modularity dissipation is proportional to the amount of additional work required to perform a computational task modularly, measuring a structural energy cost. It determines the thermodynamic efficiency of different modular implementations of the same computation, and so it has immediate consequences for the architecture of physically embedded transducers, known as information ratchets. Constructively, we show how to circumvent modularity dissipation by designing internal ratchet states that capture the information reservoir's global correlations and patterns. Thus, there are routes to thermodynamic efficiency that circumvent globally integrated protocols and instead reduce modularity dissipation to optimize the architecture of computations composed of a series of localized operations.

Key to biological success, the requisite variety that confronts an adaptive organism is the set of detectable, accessible, and controllable states in its environment. We analyze its role in the thermodynamic functioning of information ratchets—a form of autonomous Maxwellian Demon capable of exploiting fluctuations in an external information reservoir to harvest useful work from a thermal bath. This establishes a quantitative paradigm for understanding how adaptive agents leverage structured thermal environments for their own thermodynamic benefit. General ratchets behave as memoryful communication channels, interacting with their environment sequentially and storing results to an output. The bulk of thermal ratchets analyzed to date, however, assume memoryless environments that generate input signals without temporal correlations. Employing computational mechanics and a new information-processing Second Law of Thermodynamics (IPSL) we remove these restrictions, analyzing general finite-state ratchets interacting with structured environments that generate correlated input signals. On the one hand, we demonstrate that a ratchet need not have memory to exploit an uncorrelated environment. On the other, and more appropriate to biological adaptation, we show that a ratchet must have memory to most effectively leverage structure and correlation in its environment. The lesson is that to optimally harvest work a ratchet’s memory must reflect the input generator’s memory. Finally, we investigate achieving the IPSL bounds on the amount of work a ratchet can extract from its environment, discovering that finite-state, optimal ratchets are unable to reach these bounds. In contrast, we show that infinite-state ratchets can go well beyond these bounds by utilizing their own infinite “negentropy”. We conclude with an outline of the collective thermodynamics of information-ratchet swarms.

In this paper, the notion of soft normal int-field over a field has been introduced. We have established a correspondence, called Soft Galois correspondence, between the soft intermediate int-fields of a finite Galois extension and the soft intgroups of the Galois group corresponding to the field extension. We have generalized some results of Galois theory in the environment of soft set theory. [ABSTRACT FROM AUTHOR]

We introduce a family of Maxwellian Demons for which correlations among information bearing degrees of freedom can be calculated exactly and in compact analytical form. This allows one to precisely determine Demon functional thermodynamic operating regimes, when previous methods either misclassify or simply fail due to approximations they invoke. This reveals that these Demons are more functional than previous candidates. They too behave either as engines, lifting a mass against gravity by extracting energy from a single heat reservoir, or as Landauer erasers, consuming external work to remove information from a sequence of binary symbols by decreasing their individual uncertainty. Going beyond these, our Demon exhibits a new functionality that erases bits not by simply decreasing individual-symbol uncertainty, but by increasing inter-bit correlations (that is, by adding temporal order) while increasing single-symbol uncertainty. In all cases, but especially in the new erasure regime, exactly accounting for informational correlations leads to tight bounds on Demon performance, expressed as a refined Second Law of thermodynamics that relies on the Kolmogorov-Sinai entropy for dynamical processes and not on changes purely in system configurational entropy, as previously employed. We rigorously derive the refined Second Law under minimal assumptions and so it applies quite broadly - for Demons with and without memory and input sequences that are correlated or not. We note that general Maxwellian Demons readily violate previously proposed, alternative such bounds, while the current bound still holds. As such, it broadly describes the minimal energetic cost of any computation by a thermodynamic system.

Detection of gas molecules and volatile organic compounds (VOCs) using efficient, low cost sensors has fetched significant attention in environmental monitoring, safety measures and medical diagnosis. In the present work, nickel ferrite (NFO) nanoparticles are explored as p‐type semiconducting metal oxide (SMO) sensor for detection of five different organic vapors namely methanol, ethanol, n‐propanol, iso‐propanol and acetone which often cause severe damage to human body under prolonged exposure. The sensing studies in presence of the aforementioned five vapors are carried out by varying the sensor operating temperature (225–300 °C) and vapor concentrations (10–1000 ppm). Developed NFO sensor demonstrated best performance in terms of sensing (~10 ppm), response time (<10 s), excellent repeatability and selectivity towards ethanol among all other considered gas species. The repeatability of the sensor response is verified and the underlying reasons for the variation in the response of NFO sensor due to the change of operating temperature, analyte type and concentrations has been discussed. The synthesis of NFO through auto combustion method and study on their formation behaviour, oxygen vacancy evolution, band gap calculation, crystalline nature as well as microstructural features provides here the comprehensive information about the potential application of NFO nanoparticles as gas sensor. [ABSTRACT FROM AUTHOR]

Prakash Kumar,1 Pushkar Shivam,*,2 Saptarshi Mandal,*,3 Pragya Prasanna,1 Saurabh Kumar,1 Surendra Rajit Prasad,1 Ashish Kumar,4 Prolay Das,3 Vahab Ali,5 Shubhankar Kumar Singh,2 Debabrata Mandal11Department of Biotechnology, National Institute of Pharmaceutical Education and Research (NIPER) – Hajipur, Vaishali, India; 2Department of Microbiology, Rajendra Memorial Research Institute of Medical Sciences, Patna, India; 3Department of Chemistry, Indian Institute of Technology Patna, Patna, India; 4Department of Molecular Biology, Rajendra Memorial Research Institute of Medical Sciences, Patna, India; 5Department of Biochemistry, Rajendra Memorial Research Institute of Medical Sciences, Patna, India*These authors contributed equally to this workBackground: Amphotericin B (AmB) as a liposomal formulation of AmBisome is the first line of treatment for the disease, visceral leishmaniasis, caused by the parasite Leishmania donovani. However, nephrotoxicity is very common due to poor water solubility and aggregation of AmB. This study aimed to develop a water-soluble covalent conjugate of gold nanoparticle (GNP) with AmB for improved antileishmanial efficacy and reduced cytotoxicity.Methods: Citrate-reduced GNPs (∼39 nm) were functionalized with lipoic acid (LA), and the product GNP-LA (GL ∼46 nm) was covalently conjugated with AmB using carboxyl-to-amine coupling chemistry to produce GNP-LA-AmB (GL-AmB ∼48 nm). The nanoparticles were characterized by dynamic light scattering, transmission electron microscopy (TEM), and spectroscopic (ultraviolet–visible and infrared) methods. Experiments on AmB uptake of macrophages, ergosterol depletion of drug-treated parasites, cytokine ELISA, fluorescence anisotropy, flow cytometry, and gene expression studies established efficacy of GL-AmB over standard AmB.Results: Infrared spectroscopy confirmed the presence of a covalent amide bond in the conjugate. TEM images showed uniform size with smooth surfaces of GL-AmB nanoparticles. Efficiency of AmB conjugation was ∼78%. Incubation in serum for 72 h showed