NnSnRK1-NnATG1-mediated autophagic cell death governs flower bud abortion in shaded lotus.

Many plants can terminate their flowering process in response to unfavourable environments, but the mechanisms underlying this response are poorly understood. In this study, we observed that the lotus flower buds were susceptible to abortion under shaded conditions. The primary cause of abortion was excessive autophagic cell death (ACD) in flower buds. Blockade of autophagic flux in lotus flower buds consistently resulted in low levels of ACD and improved flowering ability under shaded conditions. Further evidence highlights the importance of the NnSnRK1-NnATG1 signalling axis in inducing ACD in lotus flower buds and culminating in their timely abortion. Under shaded conditions, elevated levels of NnSnRK1 activated NnATG1, which subsequently led to the formation of numerous autophagosome structures in lotus flower bud cells. Excessive autophagy levels led to the bulk degradation of cellular material, which triggered ACD and the abortion of flower buds. NnSnRK1 does not act directly on NnATG1. Other components, including TOR (target of rapamycin), PI3K (phosphatidylinositol 3-kinase) and three previously unidentified genes, appeared to be pivotal for the interaction between NnSnRK1 and NnATG1. This study reveals the role of autophagy in regulating the abortion of lotus flower buds, which could improve reproductive success and act as an energy-efficient measure in plants.

Loss of α-actinin-3 during human evolution provides superior cold resilience and muscle heat generation.

The protein α-actinin-3 expressed in fast-twitch skeletal muscle fiber is absent in 1.5 billion people worldwide due to homozygosity for a nonsense polymorphism in ACTN3 (R577X). The prevalence of the 577X allele increased as modern humans moved to colder climates, suggesting a link between α-actinin-3 deficiency and improved cold tolerance. Here, we show that humans lacking α-actinin-3 (XX) are superior in maintaining core body temperature during cold-water immersion due to changes in skeletal muscle thermogenesis. Muscles of XX individuals displayed a shift toward more slow-twitch isoforms of myosin heavy chain (MyHC) and sarcoplasmic reticulum (SR) proteins, accompanied by altered neuronal muscle activation resulting in increased tone rather than overt shivering. Experiments on Actn3 knockout mice showed no alterations in brown adipose tissue (BAT) properties that could explain the improved cold tolerance in XX individuals. Thus, this study provides a mechanism for the positive selection of the ACTN3 X-allele in cold climates and supports a key thermogenic role of skeletal muscle during cold exposure in humans.

Unveiling the potential of membrane in climate change mitigation and environmental resilience in ecosystem.

Carbon capture technologies are becoming increasingly crucial in addressing global climate change issues by lowering CO2 emissions from industrial and power generation activities. Post-combustion carbon capture, which uses membranes instead of adsorbents, has emerged as one of promising and environmentally friendly approaches among these technologies. The operation of membrane technology is based on the premise of selectively separating CO2 from flue gas emissions. This provides a number of different benefits, including improved energy efficiency and decreased costs of operation. Because of its adaptability to changing conditions and its low impact on the surrounding ecosystem, it is an appealing choice for a diverse array of uses. However, there are still issues to be resolved, such as those pertaining to establishing a high selectivity, membrane degradation, and the costs of the necessary materials. In this article, we evaluate and explore the prospective applications and roles of membrane technologies to control climate change by post-combustion carbon capturing. The primary proposition suggests that the utilization of membrane-based carbon capture has the potential to make a substantial impact in mitigating CO2 emissions originating from industrial and power production activities. This is due to its heightened ability to selectively absorb carbon, better efficiency in energy consumption, and its flexibility to various applications. The forthcoming challenges and potential associated with the application of membranes in post-carbon capture are also discussed.

Optimizing bone and biomass co-torrefaction parameters: High-performance arsenic removal from wastewater via co-torrefied bone char.

This study aimed to investigate bone char's physicochemical transformations through co-torrefaction and co-pyrolysis processes with biomass. Additionally, it aimed to analyze the carbon sequestration process during co-torrefaction of bone and biomass and optimize the process parameters of co-torrefaction. Finally, the study sought to evaluate the arsenic sorption capacity of both torrefied and co-torrefied bone char. Bone and biomass co-torrefaction was conducted at 175 °C-300 °C. An orthogonal array of Taguchi techniques and artificial neural networks (ANN) were employed to investigate the influence of various torrefaction parameters on carbon dioxide sequestration within torrefied bone char. A co-torrefied bone char, torrefied at a reaction temperature of 300 °C, a heating rate of 15 °C·min-1, and mixed with 5 g m of biomass (wood dust), was selected for the arsenic (III) sorption experiment due to its elevated carbonate content. The results revealed a higher carbonate fraction (21%) in co-torrefied bone char at 300 °C compared to co-pyrolyzed bone char (500-700 °C). Taguchi and artificial neural network (ANN) analyses indicated that the relative impact of process factors on carbonate substitution in bone char followed the order of co-torrefaction temperature (38.8%) > heating rate (31.06%) > addition of wood biomass (30.1%). Co-torrefied bone chars at 300 °C exhibited a sorption capacity of approximately 3 mg g-1, surpassing values observed for pyrolyzed bone chars at 900 °C in the literature. The findings suggest that co-torrefied bone char could serve effectively as a sorbent in filters for wastewater treatment and potentially fulfill roles such as a remediation agent, pH stabilizer, or valuable source of biofertilizer in agricultural applications.

Communication consumes 35 times more energy than computation in the human cortex, but both costs are needed to predict synapse number.

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)-consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843-1864 (1960)] and [J. Sawada, D. S. Modha, "Synapse: Scalable energy-efficient neurosynaptic computing" in Application of Concurrency to System Design (ACSD) (2013), pp. 14-15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a [Formula: see text]-fold discrepancy between biological and lowest possible values of a neuron's computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).

A 9-10-Bit Adjustable and Energy-Efficient Switching Scheme for Successive Approximation Register Analog-to-Digital Converter with One Least Significant Bit Common-Mode Voltage Variation.

A 9-10-bit adjustable and energy-efficient switching scheme for SAR ADC with one-LSB common-mode voltage variation is proposed. Based on capacitor-splitting technology and common-mode conversion techniques, the proposed switching scheme reduces the DAC switching energy by 96.41% compared to the conventional scheme. The low complexity and the one-LSB common-mode voltage offset of this scheme benefit from the simultaneous switching of the reference voltages of the capacitors corresponding to the positive array and the negative array throughout the entire reference voltage switching process, and the reference voltage of each capacitor in the scheme does not change more than two voltages. The post-layout result shows that the ADC achieves the 54.96 dB SNDR, the 61.73 dB SFDR, and the 0.67 µw power consumption with the 10-bit mode and the 48.33 dB SNDR, the 54.17 dB SFDR, and the 0.47 µw power consumption with the 9-bit mode in a 180 nm process with a 100 kS/s sampling frequency.

Energy-Efficient Edge and Cloud Image Classification with Multi-Reservoir Echo State Network and Data Processing Units.

In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning models, such as convolutional neural networks (CNNs), pose significant computational and memory challenges, limiting their use in resource-constrained environments. Echo State Networks (ESNs), based on reservoir computing principles, offer a promising alternative with reduced computational complexity and shorter training times. This study explores the applicability of ESN-based architectures in image classification and weather forecasting tasks, using benchmarks such as the MNIST, FashionMnist, and CloudCast datasets. Through comprehensive evaluations, the Multi-Reservoir ESN (MRESN) architecture emerges as a standout performer, demonstrating its potential for deployment on DPUs or home stations. In exploiting the dynamic adaptability of MRESN to changing input signals, such as weather forecasts, continuous on-device training becomes feasible, eliminating the need for static pre-trained models. Our results highlight the importance of lightweight models such as MRESN in cloud and edge computing applications where efficiency and sustainability are paramount. This study contributes to the advancement of efficient computing practices by providing novel insights into the performance and versatility of MRESN architectures. By facilitating the adoption of lightweight models in resource-constrained environments, our research provides a viable alternative for improved efficiency and scalability in modern computing paradigms.

An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs.

In the fields of industrial production or safety monitoring, wireless sensor networks are often content with unreliable and time-varying channels that are susceptible to interference. Consequently, ensuring both transmission reliability and data accuracy has garnered substantial attention in recent years. Although multipath routing-based schemes can provide transmission reliability for wireless sensor networks, achieving high data accuracy simultaneously remains challenging. To address this issue, an Energy-efficient Multipath Routing algorithm balancing data Accuracy and transmission Reliability (EMRAR) is proposed to balance the reliability and accuracy of data transmission. The multipath routing problem is formulated into a multi-objective programming problem aimed at optimizing both reliability and power consumption while adhering to data accuracy constraints. To obtain the solution of the multi-objective programming, an adaptive artificial immune algorithm is employed, in which the antibody initialization method, antibody incentive calculation method, and immune operation are improved, especially for the multipath routing scheme. Simulation results show that the EMRAR algorithm effectively balances data accuracy and transmission reliability while also saving energy when compared to existing algorithms.

Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 µs and ultra-low power consumption of 15 µW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

Multi-Hop Clustering and Routing Protocol Based on Enhanced Snake Optimizer and Golden Jackal Optimization in WSNs.

A collection of smaller, less expensive sensor nodes called wireless sensor networks (WSNs) use their sensing range to gather environmental data. Data are sent in a multi-hop manner from the sensing node to the base station (BS). The bulk of these sensor nodes run on batteries, which makes replacement and maintenance somewhat difficult. Preserving the network's energy efficiency is essential to its longevity. In this study, we propose an energy-efficient multi-hop routing protocol called ESO-GJO, which combines the enhanced Snake Optimizer (SO) and Golden Jackal Optimization (GJO). The ESO-GJO method first applies the traditional SO algorithm and then integrates the Brownian motion function in the exploitation stage. The process then integrates multiple parameters, including the energy consumption of the cluster head (CH), node degree of CH, and distance between node and BS to create a fitness function that is used to choose a group of appropriate CHs. Lastly, a multi-hop routing path between CH and BS is created using the GJO optimization technique. According to simulation results, the suggested scheme outperforms LSA, LEACH-IACA, and LEACH-ANT in terms of lowering network energy consumption and extending network lifetime.

Energy Consumption Analysis for Continuous Phase Modulation in Smart-Grid Internet of Things of beyond 5G.

Wireless sensor network (WSN) underpinning the smart-grid Internet of Things (SG-IoT) has been a popular research topic in recent years due to its great potential for enabling a wide range of important applications. However, the energy consumption (EC) characteristic of sensor nodes is a key factor that affects the operational performance (e.g., lifetime of sensors) and the total cost of ownership of WSNs. In this paper, to find the modulation techniques suitable for WSNs, we investigate the EC characteristic of continuous phase modulation (CPM), which is an attractive modulation scheme candidate for WSNs because of its constant envelope property. We first develop an EC model for the sensor nodes of WSNs by considering the circuits and a typical communication protocol that relies on automatic repeat request (ARQ)-based retransmissions to ensure successful data delivery. Then, we use this model to analyze the EC characteristic of CPM under various configurations of modulation parameters. Furthermore, we compare the EC characteristic of CPM with that of other representative modulation schemes, such as offset quadrature phase-shift keying (OQPSK) and quadrature amplitude modulation (QAM), which are commonly used in communication protocols of WSNs. Our analysis and simulation results provide insights into the EC characteristics of multiple modulation schemes in the context of WSNs; thus, they are beneficial for designing energy-efficient SG-IoT in the beyond-5G (B5G) and the 6G era.

Energy Efficient All-Electric-Field-Controlled Multiferroic Magnetic Domain-Wall Logic.

Magnetic domain wall (DW)-based logic devices offer numerous opportunities for emerging electronics applications allowing superior performance characteristics such as fast motion, high density, and nonvolatility to process information. However, these devices rely on an external magnetic field, which limits their implementation; this is particularly problematic in large-scale applications. Multiferroic systems consisting of a piezoelectric substrate coupled with ferromagnets provide a potential solution that provides the possibility of controlling magnetization through an electric field via magnetoelastic coupling. Strain-induced magnetization anisotropy tilting can influence the DW motion in a controllable way. We demonstrate a method to perform all-electrical logic operations using such a system. Ferromagnetic coupling between neighboring magnetic domains induced by the electric-field-controlled strain has been exploited to promote noncollinear spin alignment, which is used for realizing essential building blocks, including DW generation, propagation, and pinning, in all implementations of Boolean logic, which will pave the way for scalable memory-in-logic applications.

Toward a greener future: A survey on sustainable blockchain applications and impact.

Blockchain Technology has garnered significant attention due to its immense potential to transform the way transactions are conducted and information is managed. Blockchain is a decentralized digital ledger that is spread across a network of computers, ensuring the secure, transparent, and unchangeable recording of transactions. However, the energy consumption of certain blockchain networks like Bitcoin, Litecoin, Monero, Zcash, and others has generated apprehensions regarding the sustainability of this technology. Bitcoin alone consumes approximately 100 terawatt-hours annually, contributing significantly to global carbon emissions. The substantial energy requirements not only contribute to carbon emissions but also pose a risk to the long-term viability of the blockchain industry. This study reviews articles from eight reputable databases between 2017 to August 2023, employing the systematic review and preferred reporting items for systematic reviews and meta-analyses approach for screening. Therefore, explore the applications of sustainable blockchain networks aimed at reducing environmental impact while ensuring efficiency and security. This survey also assesses the challenges and limitations posed by diverse blockchain applications regarding sustainability and provides valuable foresight into potential future advancements. Through this survey, the aim is to track and verify sustainable practices, facilitating the transition to a low-carbon economy, and promoting environmental stewardship, with a specific focus on highlighting the potential of sustainable blockchain networks in enabling secure and transparent tracking of these practices. Finally, the paper sheds light on pertinent research challenges and provides a roadmap of future directions, stimulating further research in this promising field.

Dehydration-Induced Cluster Consolidation in a Metal-Organic Framework for Sieving Hexane Isomers.

Metal-organic frameworks (MOFs) that exhibit dynamic phase-transition behavior under external stimuli could have great potential in adsorptive separations. Here we report on a zinc-based microporous MOF (JNU-80) and its reversible transformation between two crystalline phases: large pore (JNU-80-LP) and narrow pore (JNU-80-NP). Specifically, JNU-80-LP can undergo a dehydration-induced cluster consolidation under heat treatment, resulting in JNU-80-NP with a reduced channel that allows exclusion of di-branched hexane isomers while high adsorption of linear and mono-branched hexane isomers. We further demonstrate the fabrication of MOF-polymer composite (JNU-80-NP-block) and its application in the purification of di-branched isomers from liquid-phase hexane mixtures (98 % di-branched) at room temperature, affording the di-branched hexane isomers with 99.5 % purity and close to 90 % recovery rate over ten cycles. This work illustrates an interesting dehydration-induced cluster consolidation in MOF structure and the ensuing channel shrinkage for sieving di-branched hexane isomers, which may have important implications for the development of MOFs with dynamic behavior and their potential applications in non-thermal driven separation technologies.

Hierarchical Nanospheres with Polycrystalline Ir&Cu and Amorphous Cu2 O toward Energy-Efficient Nitrate Electrolysis to Ammonia.

Electrochemical reduction reaction of nitrate (NITRR) provides a sustainable route toward the green synthesis of ammonia. Nevertheless, it remains challenging to achieve high-performance electrocatalysts for NITRR especially at low overpotentials. In this work, hierarchical nanospheres consisting of polycrystalline Iridium&copper (Ir&Cu) and amorphous Cu2 O (Cux Iry Oz NS) have been fabricated. The optimal species Cu0.86 Ir0.14 Oz delivers excellent catalytic performance with a desirable NH3 yield rate (YR) up to 0.423 mmol h-1  cm-2 (or 4.8 mg h-1  mgcat -1 ) and a high NH3 Faradaic efficiency (FE) over 90% at a low overpotential of 0.69 V (or 0 VRHE ), where hydrogen evolution reaction (HER) is almost negligible. The electrolyzer toward NITRR and hydrazine oxidation (HzOR) is constructed for the first time with an electrode pair of Cu0.86 Ir0.14 Oz //Cu0.86 Ir0.14 Oz , yielding a high energy efficiency (EE) up to 87%. Density functional theory (DFT) calculations demonstrate that the dispersed Ir atom provides active site that not only promotes the NO3 - adsorption but also modulates the H adsorption/desorption to facilitate the proton supply for the hydrogenation of *N, hence boosting the NITRR. This work thus points to the importance of both morphological/structural and compositional engineering for achieving the highly efficient catalysts toward NITRR.

Facile Fabrication of Monodispersed Carbon Sphere: A Pathway Toward Energy-Efficient Direct Air Capture (DAC) Using Amino Acids.

Direct removal of carbon dioxide (CO2 ) from the atmosphere, known as direct air capture (DAC) is attracting worldwide attention as a negative emission technology to control atmospheric CO2 concentrations. However, the energy-intensive nature of CO2 absorption-desorption processes has restricted deployment of DAC operations. Catalytic solvent regeneration is an effective solution to tackle this issue by accelerating CO2 desorption at lower regeneration temperatures. This work reports a one-step synthesis methodology to prepare monodispersed carbon nanospheres (MCSs) using trisodium citrate as a structure-directing agent with acidic sites. The assembly of citrate groups on the surface of MCSs enables consistent spherical growth morphology, reduces agglomeration and enhances water dispersibility. The functionalization-assisted synthesis produces uniform, hydrophilic nanospheres of 100-600 nm range. This work also demonstrates that the prepared MCSs can be further functionalized with strong Brønsted acid sites, providing high proton donation ability. Furthermore, the materials can be effectively used in a wide range of amino acid solutions to substantially accelerate CO2 desorption (25.6% for potassium glycinate and 41.1% for potassium lysinate) in the DAC process. Considering the facile synthesis of acidic MCSs and their superior catalytic efficiency, these findings are expected to pave a new path for energy-efficient DAC.

Prediction of carbon emissions peak and carbon neutrality based on life cycle CO2 emissions in megacity building sector: Dynamic scenario simulations of Beijing.

In order to design an optimal carbon peak and carbon neutralization pathway for the high-density building sector, a dynamic prediction model is established using system-dynamics coupled building life cycle carbon emission model (SD-BLCA) with consideration of future evolutionary trajectory and time constraints. The model is applied in Beijing using the SD-BLCA combined with scenario analysis and Monte Carlo methods to explore optimal trajectory for its building sector under 30-year timeframe. The results indicate that by increasing the proportion of renewable energy generation by 7% and retrofitting 60 million m2 of existing buildings, these two mature measures can offset the growth of carbon emissions and achieve the peak target by 2025. However, achieving carbon neutrality necessitates a shift from isolated technologies to a comprehensive net-zero emissions strategy. The study proposes a time roadmap that integrates a zero-carbon energy supply system and the carbon reduction measures of the whole life cycle. This strategy primarily relies on renewable sources to provide heat, power, and hydrogen, resulting in estimated reductions of 29.8 Mt, 28.1 Mt, and 0.7 Mt, respectively. Zero energy buildings, green buildings, and renovated buildings can reduce carbon emissions through their own energy-saving measures by 8.4, 18.2, and 11.8 kg/m2, respectively.

Synthesis and photometric properties of efficient white-emitting phosphor of M-AMG transition metal complexes for OLED applications.

Progression in lighting sources mainly depended on new, robust energy-efficient diodes due to their advanced photometric properties. All organic light-emitting sources are constant energy-efficient devices and will be the light of the future. We explore the potential of transition metal complexes by focusing on cobalt(II), nickel(II), and copper (II) with aminoguanidine naphthoate as white phosphors in organic light-emitting diodes (OLEDs). The phosphors synthesized at optimized temperature were characterized structurally and thermally by spectral, thermal, and diffraction techniques. The photophysical studies of the target compound in several organic solvents having divergent polarity were also studied, and the results were exhibited. Photometric properties of the complexes were studied using photoluminescence, CIE (Commission internationale de l'éclairage) chromaticity coordinates, correlated color temperature, color purity, Duv, and TLCI (Television Lighting Consistency Index) to verify the applicability of complexes as phosphors. Excellent luminescence property with a high coloring index for (Cu(2NA-AMG-2H2 O)) opens the advanced avenue for light sources and serves as vital constituents for light-emitting diodes.

An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks.

The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with the IoT, known as the SDWSN-IoT. However, wireless IoT devices deployed in such systems face limitations in the energy supply, unpredicted network changes, and the quality of service requirements. Such challenges necessitate the careful design of the underlying routing protocol, as failure to address them often results in constantly disconnected networks with poor network performance. In this paper, we present an intelligent, energy-efficient multi-objective routing protocol based on the Reinforcement Learning (RL) algorithm with Dynamic Objective Selection (DOS-RL). The primary goal of applying the proposed DOS-RL routing scheme is to optimize energy consumption in IoT networks, a paramount concern given the limited energy reserves of wireless IoT devices and the adaptability to network changes to facilitate a seamless adaption to sudden network changes, mitigating disruptions and optimizing the overall network performance. The algorithm considers correlated objectives with informative-shaped rewards to accelerate the learning process. Through the diverse simulations, we demonstrated improved energy efficiency and fast adaptation to unexpected network changes by enhancing the packet delivery ratio and reducing data delivery latency when compared to traditional routing protocols such as the Open Shortest Path First (OSPF) and the multi-objective Q-routing for Software-Defined Networks (SDN-Q).

Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework.

The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. It improves the energy efficiency of the underwater sensors personalized according to the various water depth layers of sensor locations. In particular, we integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. Mathematically, the optimal mechanism is formulated as a complex nonlinear integer programming (NIP) problem. A new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is thus further proposed to tackle this sophisticated NIP problem. The systematic simulation results demonstrate the effectiveness of our mechanism in improving the energy efficiency of UWSNs. Moreover, our presented E-DDTMD algorithm achieves significantly superior performance to the baseline schemes.