Central Unit Research Articles

Influence of perceived safety culture and safety knowledge on safety performance mediated by safety motivation

Perceived safety culture and safety knowledge are the two independent variables in this study. Safety performance is the dependent variable, and safety motivation is the mediating variable at PT. Central Kalimantan Distribution Main Unit of PLN (Persero). This research method uses a questionnaire with 101 employees of fieldwork partners at PT. PLN (Persero) Central Kalimantan Main Distribution Unit is the sample and has been selected from the population. Variable estimations utilize the LC procedure with a weight scale from 1 to 5. Utilize the partial least squares method to determine the correlation between variables X1, X2, Y, and Z. The consequences of the examination express that (1) PSC meaningfully affects SP, (2) SK affects SP, (3) SM affects SP, (4) PSC affects SM, (5) SK significantly affects SM, (6) PSC affects SP with SM Intercession and (7) SK affects SP with SM Intervention.

A review of emerging trends in photonic deep learning accelerators

Deep learning has revolutionized many sectors of industry and daily life, but as application scale increases, performing training and inference with large models on massive datasets is increasingly unsustainable on existing hardware. Highly parallelized hardware like Graphics Processing Units (GPUs) are now widely used to improve speed over conventional Central Processing Units (CPUs). However, Complementary Metal-oxide Semiconductor (CMOS) devices suffer from fundamental limitations relying on metallic interconnects which impose inherent constraints on bandwidth, latency, and energy efficiency. Indeed, by 2026, the projected global electricity consumption of data centers fueled by CMOS chips is expected to increase by an amount equivalent to the annual usage of an additional European country. Silicon Photonics (SiPh) devices are emerging as a promising energy-efficient CMOS-compatible alternative to electronic deep learning accelerators, using light to compute as well as communicate. In this review, we examine the prospects of photonic computing as an emerging solution for acceleration in deep learning applications. We present an overview of the photonic computing landscape, then focus in detail on SiPh integrated circuit (PIC) accelerators designed for different neural network models and applications deep learning. We categorize different devices based on their use cases and operating principles to assess relative strengths, present open challenges, and identify new directions for further research.

Efficient all-electron hybrid density functionals for atomistic simulations beyond 10 000 atoms.

Hybrid density functional approximations (DFAs) offer compelling accuracy for abinitio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange contribution, which is the limiting factor for the application of the method for large-scale simulations. In this work, we present a drastically optimized resolution-of-identity-based real-space implementation of the exact exchange evaluation for both non-periodic and periodic boundary conditions in the all-electron code FHI-aims, targeting high-performance central processing unit (CPU) compute clusters. The introduction of several new refined message passing interface (MPI) parallelization layers and shared memory arrays according to the MPI-3 standard were the key components of the optimization. We demonstrate significant improvements of memory and performance efficiency, scalability, and workload distribution, extending the reach of hybrid DFAs to simulation sizes beyond ten thousand atoms. In addition, we also compare the runtime performance of the PBE, HSE06, and PBE0 functionals. As a necessary byproduct of this work, other code parts in FHI-aims have been optimized as well, e.g., the computation of the Hartree potential and the evaluation of the force and stress components. We benchmark the performance and scaling of the hybrid DFA-based simulations for a broad range of chemical systems, including hybrid organic-inorganic perovskites, organic crystals, and ice crystals with up to 30 576 atoms (101 920 electrons described by 244 608 basis functions).

Efficient protein structure archiving using ProteStAr.

The introduction of Deep Minds' Alpha Fold 2 enabled the prediction of protein structures at an unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data, which hinders the effective use of predicted structures in large-scale analyses. Here, we present ProteStAr, a compressor dedicated to CIF/PDB, as well as supplementary PAE files. Its main contribution is a novel approach to predicting atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates, the largest component of the protein structure files. The compression is lossless by default, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, i.e. BinaryCIF, Foldcomp, PDC, our approach offers a superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of the multicore architecture of current central processing units and operates with speeds of about 1 GB/s. The presence of Python and C++ API further increases the usability of the presented method. The source code of ProteStAr is available at https://github.com/refresh-bio/protestar.

Platform kinship and the reshaping of political order in the Somali territories

Abstract Numerous studies show that digital technologies facilitate diaspora engagement in homeland affairs. However, communities in home countries also adapt digital platforms to harness diasporic support and drive socio-political change. Despite a rich literature on ‘digital kinship’, there remains a limited understanding of kinship's broader political and developmental impact, especially in (post)conflict regions. This article draws on fieldwork in the Somali territories to argue that a distinctive model of governance, platform kinship, is emerging as an alternative to existing state and international development programmes. Focusing on WhatsApp, it highlights how the platform's specific features are adapted to Somali segmentary clan structures, enabling kinship groups to bridge digital divides, preserve oral traditions and uphold egalitarian principles. Platform kinship has state-like effects. Through an ecosystem of WhatsApp groups, geographically dispersed kin mediate disputes, coordinate development projects, fund political campaigns, and respond to conflicts and crises. However, it also empowers new actors, including youth, politicians and business elites, while marginalizing elders. Furthermore, because platform kinship strengthens the clan as the central political unit, it can deepen divisions between kinship groups and undermine state-building projects and conceptions of national identity. This has important implications for policy-makers and academics working on digital governance, development and peacebuilding.

Efficient protein structure archiving using ProteStAr.

The introduction of Deep Minds' Alpha Fold 2 enabled the prediction of protein structures at an unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data, which hinders the effective use of predicted structures in large-scale analyses. Here, we present ProteStAr, a compressor dedicated to CIF/PDB, as well as supplementary PAE files. Its main contribution is a novel approach to predicting atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates, the largest component of the protein structure files. The compression is lossless by default, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, ie, BinaryCIF, Foldcomp, PDC, our approach offers a superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of the multicore architecture of current central processing units and operates with speeds of about 1 GB/s. The presence of Python and C ++ API further increases the usability of the presented method. The source code of ProteStAr is available at https://github.com/refresh-bio/protestar.

Complete group of central units in some group rings over Z and Z [ θ p ]

In the present work, we contribute with new results that enabled us to give an explicit description of the groups of central units of integral group rings and group rings with coefficients in Z [ θ ] , for certain groups G, where θ is a p − primitive root of unity and p is a prime number. In particular, we find a set of generators for the case where G is the Heisenberg Group.

Analysis and Development of an IoT System for an Agrivoltaics Plant

This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings.

Analysis and Development of an IoT System for an Agrivoltaics Plant

This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings.

Consistent Thermal Conductivities of Spring-Like Structured Polydimethylsiloxane Composites under Large Deformation.

Flexible and highly thermally conductive materials with consistent thermal conductivity (λ) during large deformation are urgently required to address the heat accumulation in flexible electronics. In this study, spring-like thermal conduction pathways of silver nanowire (S-AgNW) fabricated by 3D printing are compounded with polydimethylsiloxane (PDMS) to prepare S-AgNW/PDMS composites with excellent and consistent λ during deformation. The S-AgNW/PDMS composites exhibit a λ of 7.63W m-1 K-1 at an AgNW amount of 20 vol%, which is ≈42 times that of PDMS (0.18W m-1 K-1) and higher than that of AgNW/PDMS composites with the same amount and random dispersion of AgNW (R-AgNW/PDMS) (5.37W m-1 K-1). Variations in the λ of 20 vol% S-AgNW/PDMS composites are less than 2% under a deformation of 200% elongation, 50% compression, or 180° bending, which benefits from the large deformation characteristics of S-AgNW. The heat-transfer coefficient (0.29W cm-2 K-1) of 20 vol% S-AgNW/PDMS composites is ≈1.3 times that of the 20 vol% R-AgNW/PDMS composites, which reduces the temperature of a full-stressed central processing unit by 6.8°C compared to that using the 20 vol% R-AgNW/PDMS composites as a thermally conductive material in the central processing unit.

On the total energy consumption of scalable cache-aided multi-CPU cell-free massive MIMO systems

In conventional cell-free (CF) systems, the scalability and feasibility are hindered by the fact that a single central processing unit (CPU) manages all access points and the stringent synchronization requirement for coherent transmission (CT) across the entire network. Multi-CPU CF systems emerge as a scalable and feasible alternative, yet they still encounter the typical CF system challenge of high downlink transmit energy consumption from extensive fronthaul and backhaul transmissions. To tackle this issue, we propose a scalable downlink partial coherent transmission (PCT) strategy for multi-CPU CF systems to provide high spectral efficiency (SE), reduce transmission time and thereby lower energy consumption, while keeping decoding complexity manageable. Based on the PCT strategy, we introduce scalable cache-aided multi-CPU CF systems and develop the corresponding total energy consumption (TEC) model. A successive convex approximation algorithm is proposed to obtain the suboptimal cache placement to minimize the TEC. Simulation results indicate that the proposed PCT-based scalable cache-aided multi-CPU CF systems can significantly reduce the TEC and consistently maintain the TEC at a relatively low level across all Zipf parameters by leveraging the high SE of CT and the independent transmission of non-coherent transmission.

P-417 Transmyometrial embryo transfer as a reliable alternative to challenging transcervical embryo transfer

Abstract Study question Can transmyometrial embryo transfer be a reliable alternative to challenging transcervical embryo transfer Summary answer In selected cases, where transcervical embryo transfer is impossible, transmyometrial embryo transfer is a suitable alternative. What is known already Transmyometrial embryo transfer (TMET) can be a last resort option for challenging transcervical embryo transfer (ET). Indications for TMET include; failure to access the uterus transcervically due to previous cervical surgery, stenosed cervix which will not dilate or due to challenging cervical anatomy. This technique is done by passing a long needle through the myometrium to enter the uterine cavity. This can be a less traumatic option than multiple attempts at ET with the added consequences of using a tenaculum. This centre has been performing TMET since 2004. Study design, size, duration This was a retrospective audit looking at TMET cases from 2004 to 2023. 125 cases were recorded and reviewed. Participants/materials, setting, methods The results are from a single centre unit which receives referrals for TMET from outside units. This was an audit and cases were initially found by reviewing all cases of embryo transfer from 2004. Where TMET had been recorded, the notes were then collected to ascertain patient demographics, reasons for transmyometrial embryo transfer, embryo grading and pregnancy outcomes. Main results and the role of chance Since 2004, 125 cases of TMET were recorded. 16 notes were excluded as there was unclear documentation on the type of embryo transfer, leaving 109 cases. There were 23 positive pregnancy tests, 6 documented scans showing a fetal heart at 7 weeks and three documented live births. Indication for TMET was mainly a failure to enter the uterine cavity due to stenosed cervix. In all cases of TMET, patient’s had undergone multiple attempts at accessing the uterine cavity, along with multiple attempts at hysteroscopy and dilatation. Only after trying all other options was TMET used, and mainly as a last resort measure. All cases were planned and used frozen blastocysts. There were no complications to TMET. Due to the last resort nature of TMET and the reasons for undergoing assisted reproductive technology it is unlikely these pregnancies were by chance. Limitations, reasons for caution Due to the length of time this centre has been doing TMET the documentation from the earlier cases was poor and births could have been missed due to poor follow up. Training in TMET is difficult due to the low numbers per year. Wider implications of the findings TMET remains a safe and reliable alternative option when facing a challenging transcervical embryo transfer and should be used as a last resort measure. Trial registration number not applicable

Double-bridged N–B←N bipyridyl-based airfoil-shaped small molecule dyes: π-bridge regulation on photovoltaic performance

The double-bridged N–B←N bipyridine unit (BNBP) with boron-nitrogen covalent bond and coordination bond can reduce the electronic energy level of the π-conjugated system, resulting in a reduction of band gap and a red shift in the absorption spectrum. Embedding the BNBP building block into organic optoelectronic materials provides a novel possibility in molecule design. In this paper, under the guidance of density functional theory (DFT) calculations, a series of 3D “airfoil-shaped” small molecule donors (SMDs) have been designed and successfully synthesized through Sonogashira, Suzuki and Stille coupling reactions. BNBP is adopted as the central electron-withdrawing unit and strong electron-rich triphenylamine (TPA) as the terminal group. The novel “airfoil-shaped” structure plays a supporting role in reducing the carrier recombination in the active layer. It reveals that molecular design engineering can achieve the expected results by the following steps: Firstly, the introduction of electron-rich alkoxy groups into the end of TPA through side chain engineering helps to improve the solubility and film-forming properties of the materials. Then, the π-bridge regulation not only extends the conjugate structure, but also plays a crucial role in the regulation of photovoltaic properties. It is worth noting that the compound BNBP(3TTPA)2 was synthesized by introducing thiophene oligomer into BNBP-based SMDs for the first time. Finally, the compound BNBP(EDPPTPA)2 was constructed by combining BNBP with DPP dye unit. Its narrow band gap is only 1.36 eV, which is one of the narrowest band gaps in small molecular organic-boron materials. In particular, compared with the starting compound BNBP(ETPA)2, the synergistic effects of DPP and BNBP broaden the edge absorption wavelength to 814 nm, resulting in a red shift of 170 nm. The power conversion efficiency (PCE) of 4.48 % is obtained in BNBP(EDPPTPA)2-based bulk heterojunction (BHJ) organic solar cells (OSCs), which is more than twice that of reference compound BNBP(ETPA)2. This work provides important references for the future development of 3D BNBP-based organic-boron SMDs and the structural regulation of materials through molecular engineering.

Experimental study on composite spherical-roof contoured-core (SRCC) sandwich panels under the V-shaped quasi-static indentation loading

The paper investigated the experimental study of composite spherical-roof contoured-core (SRCC) sandwich panels under a V-shaped quasi-static indentation (QSI) test. The carbon fiber, glass fiber, aramid fiber, hybrid carbon/aramid, and glass fiber chopped strand mat sheets were used to manufacture the composite cores, which used the hand layup and hot compression techniques. The polylactide (PLA) core was fabricated using fused deposition modelling (FDM). The V-shaped indenter had a 65° angle and was set at 1 mm/min as indentation speed in the experimental test. It was found that the maximum indentation deflection was 35.49 mm on the SRCC sandwich panel with a hybrid carbon/aramid core compared to other composite cores. Furthermore, it was localized fiber fracture, matrix damage, delamination, inwards fronds, and debonding damage mode on composite SRCC sandwich panels, which was visually shown at the central SRCC core unit cell. It is highlighted that aramid fiber and hybrid carbon/aramid materials can provide outstanding impact characteristics, which have great potential to be used as impact-resistant sandwich structures.

OPTIMIZATION OF THE OPERATION OF AN ISOMERIZATION INSTALLATION FOR LIGHT PETROL FRACTIONS

The interest in this process lies in the fact that it has great value since low-octane components are used as raw materials - the n fraction. temperature – 62°С and catalytic reforming raffinates. This raw material contains mainly pentane and hexane fractions. This raw material, as well as fractions С5 and С6 obtained from a gas fractionation unit (GFU), and a central gas fractionation unit (CGFU), are isomerized in a hydrogen environment in the presence of a catalyst. Hydrocarbons with a relatively high octane number are obtained. When isomerizing the pentane fraction, a product with a higher octane number is obtained. Isomerization of n-pentane is of interest not only for the oil refining industry, but also for the petrochemical industry, since isopentane can be converted by dehydrogenation into isoprene, a raw material for rubber. The article conducted a study on the selection of the optimal catalyst for the light gasoline fraction isomerization unit located at the Atyrau Oil Refinery. The catalytic activity of the catalyst samples was assessed in a laboratory flow-type installation. Determination of the mass fraction of С2-С5 hydrocarbons in С5 hydrocarbon fractions (in raw materials and catalyst) was carried out by a method based on the separation of mixture components by gas-liquid chromatography. The components leaving the chromatographic column were detected using a thermal conductivity detector. The calculation of the mass fractions of components was carried out using the normalization method, taking into account correction factors. The minimum concentration of C2-С5 hydrocarbons, determined by the method used, is 0.01-0.05% wt.

Research on Ecoguard Solar Powered IOT for Efficient Garbage Tracking

Abstract: The "Eco Guard" project offers a novel way to improve the effectiveness of waste tracking and management by utilizing solar-powered Internet of things technology coupled with an ESP32 microcontroller. The inability of traditional waste management systems to precisely monitor waste levels and optimize collection routes results in resource waste and negative environmental effects.The Eco Guard project seeks to overcome these obstacles by putting in place a system that powers Internet of Things (IoT) devices with sensors for monitoring waste levels in real-time using renewable solar energy. The central processing unit is the ESP32 microcontroller, which makes it easier to gather, process, and send data to a cloud platform or centralized server.The Eco Guard project's salient characteristics comprise:Solar-Powered Mechanism: Utilizing solar energy guarantees continuous

Hybrid deep learning and optimized clustering mechanism for load balancing and fault tolerance in cloud computing.

Cloud services are one of the most quickly developing technologies. Furthermore, load balancing is recognized as a fundamental challenge for achieving energy efficiency. The primary function of load balancing is to deliver optimal services by releasing the load over multiple resources. Fault tolerance is being used to improve the reliability and accessibility of the network. In this paper, a hybrid Deep Learning-based load balancing algorithm is developed. Initially, tasks are allocated to all VMs in a round-robin method. Furthermore, the Deep Embedding Cluster (DEC) utilizes the Central Processing Unit (CPU), bandwidth, memory, processing elements, and frequency scaling factors while determining if a VM is overloaded or underloaded. The task performed on the overloaded VM is valued and the tasks accomplished on the overloaded VM are assigned to the underloaded VM for cloud load balancing. In addition, the Deep Q Recurrent Neural Network (DQRNN) is proposed to balance the load based on numerous factors such as supply, demand, capacity, load, resource utilization, and fault tolerance. Furthermore, the effectiveness of this model is assessed by load, capacity, resource consumption, and success rate, with ideal values of 0.147, 0.726, 0.527, and 0.895 are achieved.

Design and implementation of a nano-scale high-speed multiplier for signal processing applications

Digital signal processing (DSP) is an engineering field involved with increasing the precision and dependability of digital communications and mathematical processes, including equalization, modulation, demodulation, compression, and decompression, which can be used to produce a signal of the highest caliber. To execute vital tasks in DSP, an essential electronic circuit such as a multiplier plays an important role, continually performing tasks such as the multiplication of two binary numbers. Multiplier is a crucial component utilized to implement a wide range of DSP tasks, including convolution, Fourier transform, discrete wavelet transforms (DWT), filtering and dithering, multimedia information processing, and more. A multiplier device includes a clock and reset buttons for more flexible operational control. Each digital signal processor constitutes a multiplier unit. A multiplier unit functions entirely autonomously from the central processing unit (CPU); consequently, the CPU is burdened with a significantly reduced amount of work. Since DSP algorithms must constantly carry out multiplication tasks, the employment of a high-speed multiplier to execute fast-speed filtering processes is vital. The previous multipliers had lots of weaknesses, such as high energy, low speed, and high area, because they implemented this necessary circuit based on traditional technology such as complementary metal-oxide semiconductor (CMOS) and very large-scale integration (VLSI). To solve all previous drawbacks in this necessary circuit, we can use nanotechnology, which directly affects the performance of the multiplier and can overcome all previous issues. One of the alternative nanotechnologies that can be used for designing digital circuits is quantum dot cellular automata, which is high speed, low area, and low power. Therefore, this manuscript suggests a quantum technology-based multiplier for DSP applications. In addition, some vital circuits, such as half adder, full adder, and ripple carry adder (RCA), are suggested for designing a multiplier. Moreover, a systolic array, accumulator, and multiply and accumulate (MAC) unit are proposed based on the quantum technology-based multiplier. Nonetheless, each of the suggested frameworks has a coplanar configuration without rotated cells. The suggested structure is developed and verified utilizing the QCADesigner 2.0.3 tools. The findings showed that all circuits have no complicated configuration, including a higher number of quantum cells, latency, and an optimum area.

Microfluidic Platform with Precisely Controlled Hydrodynamic Parameters and Integrated Features for Generation of Microvortices to Accurately Form and Monitor Biofilms in Flow.

Microorganisms often live in habitats characterized by fluid flow, and their adhesion to surfaces in industrial systems or clinical settings may lead to pipe clogging, microbially influenced corrosion, material deterioration, food spoilage, infections, and human illness. Here, a novel microfluidic platform was developed to investigate biofilm formation under precisely controlled (i) cell concentration, (ii) temperature, and (iii) flow conditions. The developed platform central unit is a single-channel microfluidic flow cell designed to ensure ultrahomogeneous flow and condition in its central area, where features, e.g., with trapping properties, can be incorporated. In comparison to static and macroflow chamber assays for biofilm studies, microfluidic chips allow in situ monitoring of biofilm formation under various flow regimes and have better environment control and smaller sample requirements. Flow simulations and experiments with fluorescent particles were used to simulate bacteria flow in the platform cell for calculating flow velocity and direction at the microscale level. The combination of flow analysis and fluorescent strain injection in the cell showed that microtraps placed at the center of the channel were efficient in capturing bacteria at determined positions and to study how flow conditions, especially microvortices, can affect biofilm formation. The microfluidic platform exhibited improved performances in terms of homogeneity and robustness for in vitro biofilm formation. We anticipate the presented platform to be suitable for broad, versatile, and high-throughput biofilm studies at the microscale level.

Microbial Proxies for Anoxic Microsites Vary with Management and Partially Explain Soil Carbon Concentration.

Anoxic microsites are potentially important but unresolved contributors to soil organic carbon (C) storage. How anoxic microsites vary with soil management and the degree to which anoxic microsites contribute to soil C stabilization remain unknown. Sampling from four long-term agricultural experiments in the central United States, we examined how anoxic microsites varied with management (e.g., cultivation, tillage, and manure amendments) and whether anoxic microsites determine soil C concentration in surface (0-15 cm) soils. We used a novel approach to track anaerobe habitat space and, hence, anoxic microsites using DNA copies of anaerobic functional genes over a confined volume of soil. No-till practices inconsistently increased anoxic microsite extent compared to conventionally tilled soils, and within one site organic matter amendments increased anaerobe abundance in no-till soils. Across all long-term tillage trials, uncultivated soils had ∼2-4 times more copies of anaerobic functional genes than their cropland counterparts. Finally, anaerobe abundance was positively correlated to soil C concentration. Even when accounting for other soil C protection mechanisms, anaerobe abundance, our proxy for anoxic microsites, explained 41% of the variance and 5% of the unique variance in soil C concentration in cropland soils, making anoxic microsites the strongest management-responsive predictor of soil C concentration. Our results suggest that careful management of anoxic microsites may be a promising strategy to increase soil C storage within agricultural soils.