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Conventional Al-air battery has many disadvantages for miniwatt applications, such as the complex water management, bulky electrolyte storage and potential leakage hazard. Moreover, the self-corrosion of Al anode continues even when the electrolyte flow is stopped, leading to great Al waste. To tackle these issues, an innovative cotton-based aluminum-air battery is developed in this study. Instead of flowing alkaline solution, cotton substrate pre-deposited with solid alkaline is used, together with a small water reservoir to continuously wet the cotton and dissolve the alkaline in-situ. In this manner, the battery can be mechanically recharged by replacing the cotton substrate and refilling the water reservoir, while the thick aluminum anode can be reused for tens of times until complete consumption. The cotton substrate shows excellent ability for the storage and transportation of alkaline electrolyte, leading to a high peak power density of 73 mW cm−2 and a high specific energy of 930 mW h g−1. Moreover, the battery discharge capacity is found to be linear to the loading of pre-deposited alkaline, so that it can be precisely controlled according to the mission profile to avoid Al waste. Finally, a two-cell battery pack with common water reservoir is developed, which can provide a voltage of 2.7 V and a power output of 223.8 mW. With further scaling-up and stacking, this cotton-based Al-air battery system with low cost and high energy density is very promising for recharging miniwatt electronics in the outdoor environment.

In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e., a verb branch and a noun branch. However, the mechanism to suppress distracting objects and exploit local human-object correlations is missing. To this end, we introduce two extra sources of information, i.e., the candidate objects' spatial location and their discriminative features, to enable concentration on the occurring interactions. We design a Symbiotic Attention withObject-centric featureAlignmentframework (SAOA) to provide meticulous reasoning between the actor and the environment. First, we introduce an object-centric feature alignment method to inject the local object features to the verb branch and noun branch. Second, we propose a symbiotic attention mechanism to encourage the mutual interaction between the two branches and select the most action-relevant candidates for classification. The framework benefits from the communication among the verb branch, the noun branch, and the local object information. Experiments based on different backbones and modalities demonstrate the effectiveness of our method. Notably, our framework achieves the state-of-the-art on the largest egocentric video dataset.

We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360$^\circ$ RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360$^\circ$ spherical image from a fisheye camera and encoder values from the robot's wheels. Our dataset incorporates data from traditionally underrepresented scenes such as indoor environments and pedestrian areas, all from the ego-perspective of the robot, both stationary and navigating. The dataset has been annotated with over 2.3 million bounding boxes spread over 5 individual cameras and 1.8 million associated 3D cuboids around all people in the scenes totaling over 3500 time consistent trajectories. Together with our dataset and the annotations, we launch a benchmark and metrics for 2D and 3D person detection and tracking. With this dataset, which we plan on extending with further types of annotation in the future, we hope to provide a new source of data and a test-bench for research in the areas of egocentric robot vision, autonomous navigation, and all perceptual tasks around social robotics in human environments.

We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in drastic differences in resolution between lower and upper body. We propose an encoder-decoder architecture with a novel multi-branch decoder designed to account for the varying uncertainty in 2D predictions. The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches. To tackle the lack of labelled data we also introduced a large photo-realistic synthetic dataset. xR-EgoPose offers high quality renderings of people with diverse skintones, body shapes and clothing, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of theart results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint., 14 pages. arXiv admin note: substantial text overlap with arXiv:1907.10045

In this work, we consider transferring the structure information from large networks to compact ones for dense prediction tasks in computer vision. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the distillation scheme for image classification and perform knowledge distillation for each pixel separately, leading to sub-optimal performance. Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem. Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pair-wise similarities by building a static graph; and ii) holistic distillation that uses adversarial training to distill holistic knowledge. The effectiveness of our knowledge distillation approaches is demonstrated by experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection. Code is available at: https://git.io/StructKD, Comment: v1:10 pages cvpr2019 accepted; v2:15 pages for a journal version; Code is available at: https://github.com/irfanICMLL/structure_knowledge_distillation; fix typos

Summary This is the second of a three-part series that charts the history of minimal access surgery from antiquity to current times. Although rapid developments in laparoscopic and robotic surgery have transformed surgical care over the last 30 years, our predecessors made significant advances in their time which set the principles for modern practice. Part I of this series described how ancient medical practitioners developed simple instruments, from metal or wood, for viewing body cavities. Improvements in the use of metal, glass and lighting allowed for inspection of deeper parts of the body. This second part of the series will show how advances in electrical technology allowed the development of improved lighting for endoscopy and laparoscopy along with the use of electrocautery for a wide range of therapeutic procedures.

This review identifies the three-dimensional knee loads that have the highest risk of injuring the anterior cruciate ligament (ACL) in the athlete. It is the combination of the muscular resistance to a large knee flexion moment, an external reaction force generating knee compression, an internal tibial torque, and a knee abduction moment during a single-leg athletic manoeuvre such as landing from a jump, abruptly changing direction, or rapidly decelerating that results in the greatest ACL loads. While there is consensus that an anterior tibial shear force is the primary ACL loading mechanism, controversy exists regarding the secondary order of importance of transverse-plane and frontal-plane loading in ACL injury scenarios. Large knee compression forces combined with a posteriorly and inferiorly sloped tibial plateau, especially the lateral plateau-an important ACL injury risk factor-causes anterior tibial translation and internal tibial rotation, which increases ACL loading. Furthermore, while the ACL can fail under a single supramaximal loading cycle, recent evidence shows that it can also fail following repeated submaximal loading cycles due to microdamage accumulating in the ligament with each cycle. This challenges the existing dogma that non-contact ACL injuries are predominantly due to a single manoeuvre that catastrophically overloads the ACL.

In this paper, we have the percussive rotational drilling regime that is determined by the combination of the rotational speed of the tool, the axial force applied on the rock the optimal values of these parameters d epend in particular on the type of tool and on the physico mechanical properties of the rocks to be felled. The aim of the research is to improve the operating regimes of percussive d rilling machines under the conditions of the Algerian limestone quarries (Hadjar soud quarry) and to obtain a better compromise between the technical and economic indices, in order to ensure a minimum cost price for a meter of drilled hole. The experimenta l results carried that this methodology, which consists of assuming that under the concrete conditions can find the combination of the setting parameters that ensure the best techno economic indices, as well as to carpenter the cost price for a meter of dr illed holes.

We propose a novel logic, called Frame Logic (FL), that extends first-order logic (with recursive definitions) using a construct Sp(.) that captures the implicit supports of formulas -- the precise subset of the universe upon which their meaning depends. Using such supports, we formulate proof rules that facilitate frame reasoning elegantly when the underlying model undergoes change. We show that the logic is expressive by capturing several data-structures and also exhibit a translation from a precise fragment of separation logic to frame logic. Finally, we design a program logic based on frame logic for reasoning with programs that dynamically update heaps that facilitates local specifications and frame reasoning. This program logic consists of both localized proof rules as well as rules that derive the weakest tightest preconditions in FL., This manuscript is an extended and revised version of the publication with the same title that appeared at ESOP 2022 (https://doi.org/10.1007/978-3-030-44914-8_19). It is currently under review

Saat ini, isu penggunaan energi yang berlebihan telah mewabah di masyarakat. Penggunaan energi yang berlebihan banyak ditemukan pada lingkungan rumah tangga. Penerapan rumah yang hemat energi dapat dijadikan solusi dalam mengatasi masalah tersebut. Namun, beberapa masyarakat tidak memprioritaskan konsep tersebut. Penelitian ini dimaksudkan untuk menentukan bagaimana persepsi masyarakat mengenai kepentingan menerapkan konsep rumah hemat energi pada rumah tinggal sehari – hari dan menentukan hal – hal yang perlu diperhatikan dalam menciptakan konsep rumah hemat energi. Metode pengumpulan data dilakukan melalui kuesioner ke beberapa responden lapisan masyarakat baik itu karyawan, wiraswasta, maupun mahasiswa. Data yang telah terkumpul dianalisis menggunakan analisis kualitatif data teks. Hasil dari analisis tersebut akan menghasilkan faktor – faktor yang menjadi alasan kepentingan menerapkan konsep rumah hemat energi dan kriteria dalam penerapan rumah hemat energi.

This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.

In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

Gas hydrates are regarded as one of the most promising alternative sources of energy, which have the potential to address the energy demand of a contemporary society. Based on the field explorations in the Eastern Nankai Trough (Japan), a multilayered hydrate reservoir model has been conceptualised and its behaviours during depressurisation production are simulated. This model incorporates the effects of the initial reservoir temperature and permeability on the mechanism of hydrate dissociation, which in turn affects the gas production. It is shown that the dissociation process is largely affected by the initial temperature distribution within the reservoir layers, and the ‘warmer’ reservoirs show (consistently) higher production potential. Furthermore, the gas production could be improved significantly, by increasing the permeability of the wellbore region, which can be achieved through the fracturing process. The close match between the simulation results and the field tests is noteworthy. The proposed multilayered model would be quite useful for analysing the efficacy of the ‘production strategy’, in most real-life situations.

The impact of gas hydrate dissociation on submarine landslides has become an increasingly significant issue. However, previous models cannot interpret the newly discovered non-conformity between the slide surface and the base of the gas hydrate stability zone. Here, the authors design an experimental approach for visualising fluid migration during hydrate dissociation at atmospheric pressure. The results identify the lateral and vertical fluid migration pathways. Weak layers propagate nearly laterally beyond the hydrate zone, which lengthens the potential slide surface. Vertical pathways branch and deviate from their original paths, generating a shallower slide surface. Furthermore, the findings show that fluid overpressure under the overlying layer leads to steepening slopes. The lubrication effect and the underestimated inclination of slopes at failure may account for the low-angle failure of clay slopes. The results support a new conceptual model that reconciles the non-conformity between the slide surface and the base of the gas hydrate stability zone and offer a new perspective for the large-scale failure of low-angle submarine slopes. It will be interesting and important to confirm these implications in the future.

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading the compute-intensive tasks to an MEC network for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. In this paper, we study a novel delay-aware DNN inference throughput maximization problem by accelerating each DNN inference through jointly exploring DNN partitioning and multi-thread parallelism. Specifically, we consider the problem under both offline and online request arrival settings: a set of DNN inference requests is given in advance, and a sequence of DNN inference requests arrives one by one without the knowledge of future arrivals, respectively. We first show that the defined problems are NP-hard. We then devise a novel constant approximation algorithm for the problem under the offline setting. We also propose an online algorithm with a provable competitive ratio for the problem under the online setting. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

PurposeThis study aims to assess and decompose the sustainable development using the 17 sustainable development goals (SDGs) in Iran in 2018, for proposing agenda-setting of public policy.Design/methodology/approachIt ranks the SDGs not only in Iran but also in the region and the world to reveal the synergetic effects.FindingsBased on the results, subaltern-populace generally suffers from the hegemonic domination of ruling elite-bourgeois, lack of strong institutions, heterogeneous policy networks and lack of advocacy role of non-governmental organizations, due to no transparency, issues in law or no rule of law, no stringent regulation, rent, suppression and Mafia, all leading to corruption and injustice.Practical implicationsTo stop the loop of corruption-injustice, Iran should homogenize the structure of the policy network. Furthermore, the failed SDGs of the three-geographic analysis are the same in a character; all of them propose SDG 3, good health and well-being as a serious failed goal.Social implicationsIn this regard, strong evidence is the pandemic Coronavirus, COVID 19 since 2019, due to its highly-disastrous consequences in early 2020 where the public policymakers could not adopt policies promptly in the glob, particularly in Iran.Originality/valueIn Iran, in addition to this, the malfunction of health is rooted in “subjective well-being” and “traffic deaths,” respectively. Concerning the transportations system in Iran, it is underscored that it is damaging the sustainable development from all the three pillars of sustainable development including, economic, social and environmental.

Displacement has marked the individual and collective lives of Puerto Ricans in Chicago, especially those who migrated in the 1950s and 1960s. For these older persons, the arrival of the gentry and the yuppies of yesterday, the hipsters of today, and the disappearance of familiar faces in their current neighborhoods are not new phenomena, but rather parts of a profoundly familiar process. They came of age in displacement. Today some Puerto Rican older adults have achieved housing security and are able to age in place because they live in low-income senior housing. Yet a sense of displacement still looms large in their daily lives with the upscaling of and new-build gentrification in their current neighborhood. This work sheds light on the meaning of place for older adult Puerto Ricans who have experienced what psychiatrist and urban studies scholar Mindy T. Fullilove calls a history of “serial displacement.” Through life history narratives and ethnographic snapshots, this paper highlights the neglected reality of “aging in displacement,” or the experience of growing up and growing older in a context of repeated socio-spatial dislocation and how individual and collective life histories of community upheaval texture the spatial and social meanings of place.

Atmospheric CO2 concentrations are soaring due to the continued use of fossil fuels in energy production, an anthropogenic activity that is playing a leading role in global warming. Thus, research aimed at the capture and conversion of CO2 into value-added products, such as cyclic carbonates, is booming. While CO2 is an abundant, cheap, non-toxic, and readily accessible C1 feedstock, its thermodynamic stability necessitates the development of highly efficient catalysts that are able to promote chemical reactions under mild conditions. In this work, a novel mesoporous poly(ionic liquid) with dual active sites was synthesized through a facile method that involves co-polymerization, post-synthetic metalation, and supercritical CO2 drying. Due to a high density of nucleophilic and electrophilic sites, the as-prepared poly(ionic liquid), denoted as P2D-4BrBQA-Zn, offers excellent performance in a CO2 cycloaddition reaction using epichlorohydrin as the substrate (98.9% conversion and 96.9% selectivity). Moreover the reaction is carried out under mild, solvent-free, and additive-free conditions. Notably, P2D-4BrBQA-Zn also efficiently promotes the conversion of various other epoxide substrates into cyclic carbonates. Overall, the catalyst is found to have excellent substrate compatibility, stability, and recyclability.

The proliferation of enterprise social media generates an ever-growing record of digital traces that provides ample opportunities to study the social making of organisations. Subsequently, we present the social fabric framework, which comprises a structured five-step approach for eliciting, interpreting, and representing the situated social idiosyncrasies and underlying patterns of the social making of organisations. The paper focuses on the application of the social fabric framework as a research method. However, the framework also lends itself to practice as a diagnostic tool that can detect emergent changes in the social fabric of an organisation as well as support organisational development and change. Moreover, by providing a vocabulary for articulating the social making of organisations, the framework can help organisation members reify their dispositions, make sense of the social dynamics, and enable a constructive discussion at the grassroots level about any controversy or aspiration.

Social touch is essential to human development and communication. Mediated social touch is suggested as a solution for circumstances where distance prevents skin-to-skin contact. However, past research aimed at demonstrating efficacy of mediated touch in reducing stress and promoting helping have produced mixed findings. These inconsistent findings could possibly be due to insufficient control of contextual factors combined with unnatural interaction scenarios. For example, touch occurs less frequently among strangers and is often accompanied with nonverbal visual cues. We investigated how visual presentation of touch, and interpersonal relationship to the sender influence perception, affective experiences, and autonomic responses the touch evoke. Fifty couples of mixed gender were recruited. A mediated touch was repeatedly applied by either the male partner or male confederate to female participants. The latter witnessed through a webcam as the sender caressed a rubber hand or touchpad to send the touch. Following our hypotheses, touch sent by one's partner was perceived softer and more comforting than stranger touch. The partner's touch also resulted in weaker skin conductance responses, particularly when sent by touching a touchpad. In sum, how a mediated touch is experienced depends both on who is touching, and on how the touch is visually represented.

Language carries implicit human biases, functioning both as a reflection and a perpetuation of stereotypes that people carry with them. Recently, ML-based NLP methods such as word embeddings have been shown to learn such language biases with striking accuracy. This capability of word embeddings has been successfully exploited as a tool to quantify and study human biases. However, previous studies only consider a predefined set of biased concepts to attest (e.g., whether gender is more or less associated with particular jobs), or just discover biased words without helping to understand their meaning at the conceptual level. As such, these approaches can be either unable to find biased concepts that have not been defined in advance, or the biases they find are difficult to interpret and study. This could make existing approaches unsuitable to discover and interpret biases in online communities, as such communities may carry different biases than those in mainstream culture. This paper improves upon, extends, and evaluates our previous data-driven method to automatically discover and help interpret biased concepts encoded in word embeddings. We apply this approach to study the biased concepts present in the language used in online communities and experimentally show the validity and stability of our method

The interrelationship between rural, rurality and social innovation still remains underexplored even though there has been considerable work within the individual discourses of social innovation and rural studies. This paper aims at broadening the scope of this interrelationship by exploring the nuanced dynamics and complexities of rural lifeworld and the experiences and knowledges of actors that shape the understanding of rural social innovation. At the core of this analysis lies two key ideas including a) an understanding that rural groups' and communities' complexities and marginalities might not be adequately understood without addressing the complex ways in which identities of caste, race, gender, ethnicity and class intersect and b) that the locally-rooted solutions resulting from these concerns are usually underrepresented in the social innovation literature and the relevance of the same needs to be recognized. This reimagining of rural social innovation is located within the theoretical perspectives of intersectionality and feminist perspective and the epistemologies of the South focusing on ‘ecologies of knowledges’ that are capable of complexifying and adding to the contemporary debates on social innovation. From an understanding of the above, the authors argue that strategies and innovations grounded on the specific groups' and communities' own knowledge and rhythm within complex rural contexts needs be recognized as social innovation.

Temporal point process is widely used for sequential data modeling. In this paper, we focus on the problem of modeling sequential event propagation in graph, such as retweeting by social network users, news transmitting between websites, etc. Given a collection of event propagation sequences, conventional point process model consider only the event history, i.e. embed event history into a vector, not the latent graph structure. We propose a Graph Biased Temporal Point Process (GBTPP) leveraging the structural information from graph representation learning, where the direct influence between nodes and indirect influence from event history is modeled respectively. Moreover, the learned node embedding vector is also integrated into the embedded event history as side information. Experiments on a synthetic dataset and two real-world datasets show the efficacy of our model compared to conventional methods and state-of-the-art., Comment: 9 pages, 6 figures, 2 tables

Electrochemical CO2 reduction (CO2RR) over molecular catalysts is a paramount approach for CO2 conversion to CO. Herein, we report a novel phthalocyanine-derived catalyst synthesized by a two-step method with a much improved electroconductivity. Furthermore, the catalyst contains both Ni–N4 sites and highly dispersed metallic Ni nanoclusters, leading to an increased CO2RR currents by two folds. Isotope labelling study and in situ spectroscopic analysis demonstrate that the existence of metallic Ni nanoclusters is the key factor for the activity enhancement and can shift the CO2RR mechanism from being electron transfer (ET)-limited (forming ∗COO−) to concerted proton-electron transfer (CPET)-limited (forming CO).

In this article, we investigate the problem of social link inference in a target location-aware social network (LSN), which aims at predicting the unobserved links between users within the network. This problem is critical for downstream applications, including network completion and friend recommendation. In addition to the network structures commonly used in general link prediction, the studies tailored for social link inference in an LSN leverage user trajectories from the spatial aspect. However, the temporal factor lying in user trajectories is largely overlooked by most of the prior studies, limiting the capabilities of capturing the temporal relevance between users. Moreover, effective user matching by fusing different views, i.e., social, spatial, and temporal factors, remains unresolved, which hinders the potential improvement of link inference. To this end, this article devises a novel multiview matching network (MVMN) by regarding each of the three factors as one view of any target user pair. MVMN enjoys the flexibility and completeness of modeling each factor by developing its suitable matching module: 1) location matching module; 2) time-series matching module; and 3) relation matching module. Each module learns a view-specific representation for matching, and MVMN fuses them for final link inference. Extensive experiments on two real-world data sets demonstrate the superiority of our approach against several competitive baselines for link prediction and sequence matching, validating the contribution of its key components.

The adaptive neurofuzzy inference system (ANFIS) is a structured multioutput learning machine that has been successfully adopted in learning problems without noise or outliers. However, it does not work well for learning problems with noise or outliers. High-accuracy real-time forecasting of traffic flow is extremely difficult due to the effect of noise or outliers from complex traffic conditions. In this study, a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), is proposed to capture the correlations among traffic flow data and, thereby, improve the accuracy of traffic flow forecasting. The new learning system adopts a fantastic objective function that minimizes both the mean and the variance of the model bias. The results from an experiment based on real-world traffic flow data showed that, compared with some kernel-based approaches, neural network approaches, and conventional ANFIS learning systems, the proposed probabilistic R-ELANFIS achieves competitive performance in terms of forecasting ability and generalizability.

In the conversion process of syngas-to-C2 species, the OH species are inevitably produced accompanying the production of key intermediates CHx(x = 1–3), traditionally, the function of surface OH species is generally accepted as the hydrogenating reactive species. This work for the first time proposed and confirmed the assisted catalytic mechanism of surface OH species that performed as the promoter for syngas-to-C2 species on Cu-based catalysts. DFT and microkinetic modeling results reveal that the produced OH species accompanying the intermediates CHx production on the MCu (M = Co, Fe, Rh) catalysts can stably exist to form OH/MCu catalysts, on which the presence of surface OH species as the promoter not only presented better activity and selectivity toward CHx(x = 1–3) compared to MCu catalysts, but also significantly suppressed CH3OH production, providing enough CHx sources to favor the production of C2 hydrocarbons and oxygenates. Correspondingly, the electronic properties analysis revealed the essential relationship between the electronic feature of OH/MCu catalysts and catalytic performance, attributing to the unique electronic micro−environment of the catalysts under the interaction of surface OH species. This new mechanism is called as OH-assisted catalytic mechanism, which may be applied in the reaction systems related to the generation of OH species.

Synthesizing nitrogen (N)-containing molecules from biomass derivatives is a new strategy for production of this kind of chemicals. Herein, for the first time we present the synthesis of N-substituted aryl pyrroles via reductive amination/cyclization of levulinic acid (LA) with primary aromatic amines and hydrosilanes (e.g., PMHS) over CsF, and a series of N-substituted aryl pyrroles could be obtained in good to excellent yields at 120 °C. The mechanism investigation indicates that the reaction proceeds in two steps: the cyclization between amine and LA occurs first to form intermediate 5-methyl-N-alkyl-1,3-dihydro-2H-pyrrolones and their isomeride (B), and then the chemo- and region-selective reduction of intermediates take place to produce the final products. This approach for synthesis of N-substituted aryl pyrroles can be performed under mild and green conditions, which may have promising applications.

Effective and robust electrocatalysts are mainly based on innovative materials and unique structures. Herein, we designed a flakelike cobalt phosphide-based catalyst supporting on NiCo2O4 nanorods array, which in-situ grew on the nickel foam (NF) current collector, referring as N–Co2P/NiCo2O4/NF electrode. By optimizing the microstructure and electronic structure through 3D hierarchy fabrication and nitrogen doping, the catalyst features with abundant electrochemical surface area, favorable surface wettability, excellent electron transport, as well as tailored d band center. Consequently, the as-prepared N–Co2P/NiCo2O4/NF electrode exhibits an impressive HER activity with a low overpotentials of 58 mV at 10 mA cm−2, a Tafel slop of 75 mV dec−1, as well as superior durability in alkaline medium. This work may provide a new pathway to effectively improve the hydrogen evolution performance of transition metal phosphides and to develop promising electrodes for practical electrocatalysis.

This work includes fabrication and electrical characterization of Metal/Interlayer/Semiconductor (MIS) structures with methyl violet organic film on p-InP wafer. Metal(Ag)/ Interlayer (methyl violet =MV)/Semiconductor(p-InP) MIS structure presents a rectifying contact behavior. The values of ideality factor (n) and barrier height (BH) for the Ag/MV/p-InP MIS diode by using the current-voltage (I-V) measurement have been extracted as 1.21 and 0.84 eV, respectively. It was seen that the BH value of 0.84 eV calculated for the Ag/MV/p-InP MIS structure was significantly higher than the value of 0.64 eV of Ag/p-InP control contact. This situation was ascribed to the fact that the MV organic interlayer increased the effective barrier height by influencing the space charge region of inorganic semiconductor. The values of diffusion potential and barrier height for the Ag/MV/p-InP MIS diode by using the capacitance-voltage (C-V) measurement have been extracted as 1.21 V and 0.84 eV, respectively. The interface-state density of the Ag/MV/p-InP structure was seen to change from 2.57×1013 eV-1cm-2 to 2.19×1012 eV-1cm-2.

Data sparsity is a common issue for most recommender systems and can severely degrade the usefulness of a system. One of the most successful solutions has been cross-domain recommender systems, which supplement the sparse data of the target domain with knowledge transferred from a source domain rich with data that is in some way related. However, there are three challenges that, if overcome, could significantly improve the quality and accuracy of cross-domain recommendation: 1) ensuring latent feature spaces of the users and items are both maximally matched; 2) taking consideration of user-item relationship and their interaction in modelling user preference; 3) enabling a two-way cross-domain recommendation that both the source and the target domains benefit from a knowledge exchange. Hence, in this paper, we propose a novel deep neural network called Dual Adversarial network for Cross-Domain Recommendation. By training the shared encoders with a domain discriminator via dual adversarial learning, the latent feature spaces for both the users and items are maximally matched. Allowing the two domains to collaboratively benefit from each other results in better recommendations for both domains. Extensive experiments with real-world datasets on six tasks demonstrate that DA-CDR significantly outperforms seven state-of-the-art baselines.

The rise of the digital society is accompanied by incalculable social risks, but very little IS research has examined the implications of the new digital society. Drawing on concepts from Beck’s critical theory of the risk society and critical discourse analysis, this study examines the public discourse on risk events during the launch of NemID, a personal digital identifier for Danish citizens. This research illustrates our difficulties and challenges in managing some of the fundamental social risks from societal digitalisation. Limited institutional capabilities for digital technologies force public officials to depend on private companies motived by profit instead of the public interest. Beliefs in digital technology as the primary determinant of social and economic progress also present many public management dilemmas. When digital risk events occur and citizens’ fears are stoked by news media and public discourse, public officials seem to have no other strategy for managing the escalating fears than systematically distorted communication. The continued rise of the digital risk society demands that IS research respond to the challenge of generating knowledge for its public management.

Owing to the advantages of high operating voltage, environmental benignity, and low cost, potassium-based dual-ion batteries (KDIBs) have been considered as a potential candidate for large-scale energy storage. However, KDIBs generally suffer from poor cycling performance and unsatisfied capacity, and inactive components of conductive agents, binders, and current collector further lower their overall capacity. Herein, we prepare coral-like carbon nanowires (CCNWs) doped with nitrogen as a binder-free anode material for K+-ion storage, in which the unique coral-like porous nanostructure and amorphous/short-range-ordered composite feature are conducive to enhancing the structural stability, to facilitating the ion transfer and to boosting the full utilization of active sites during potassiation/de-potassiation process. As a result, the CCNW anode possesses a hybrid K+-storage mechanism of diffusive behavior and capacitive adsorption, and stably delivers a high capacity of 276 mAh g-1 at 50 mA g-1, good rate capability up to 2 A g-1, and long-term cycling stability with 93 % capacity retention after 2000 cycles at 1 A g-1. Further, assembling this CCNW anode with an environmentally benign expanded graphite (EG) cathode yields a proof-of-concept KDIB, which shows a high specific capacity of 134.4 mAh g-1 at 100 mA g-1, excellent rate capability of 106.5 mAh g-1 at 1 A g-1, and long-term cycling stability over 1000 cycles without negligible capacity loss. This study provides a feasible approach to developing high-performance anodes for potassium-based energy storage devices.

Co–N–C is a promising oxygen electrochemical catalyst due to its high stability and good durability. However, due to the limited adsorption ability improvement for oxygen-containing intermediates, it usually exhibits inadequate catalytic activity with 2-electron pathway and high selectivity of hydrogen peroxide. Herein, the adsorption of Co–N–C to these intermediates is modulated by constructing heterostructures using transition metals and their derivatives based on d-band theory. The heterostructured nanobelts with MoC core and pomegranate-like carbon shell consisting of Co nanoparticles and N dopant (MoC/Co–N–C) are engineered to successfully modulate the d band center of active Co–N–C sites, resulting in a remarkably enhanced electrocatalysis performance. The optimally performing MoC/Co–N–C exhibits outstanding bi-catalytic activity and stability for the oxygen electrochemistry, featuring a high wave-half potential of 0.865 V for the oxygen reduction reaction (ORR) and low overpotential of 370 mV for the oxygen evolution reaction (OER) at 10 mA cm−2. The zinc air batteries with the MoC/Co–N–C catalyst demonstrate a large power density of 180 mW cm−2 and a long cycling lifespan (2000 cycles). The density functional theory calculations with Hubbard correction (DFT + U) reveal the electron transferring from Co to Mo atoms that effectively modulate the d band center of the active Co sites and achieve optimum adsorption ability with “single site double adsorption” mode.

Plasma methods are given significant attention in the context of conditioning the producer gas derived from biomass gasification. The goal of this work is to present the impact of hydrogen on the other producer gas compounds during microwave plasma valorization. These compounds include main producer gas components (CO, CO2, CH4, N2) and minor impurities (tar compounds, H2S and NH3). The results prove a beneficial impact of hydrogen addition on the conversion of CH4 and toluene, increasing it from ca. 68%–95% and ca. 97%–100%, respectively. Additionally, the presence of hydrogen changes the distribution of the products, inhibiting soot and aromatics production and promoting C2 compounds. In the case of CO2, the conversion increases from ca. 18%–63% when compared to nitrogen plasma, with CO being the resulting product. The presence of hydrogen inhibits H2S conversion and does not affect CO and NH3

Rural social enterprises are increasingly recognized as organisations that contribute to local development by providing goods and/or services to meet community needs and by fostering inclusive social and governance relations. The purpose of this paper is to explore how rural social enterprises engage in a plurality of socio-economic relations with different dimensions of their ‘place’ when contributing to the development of their localities. Based on three in-depth case studies of social enterprises operating in rural Ireland, our findings illustrate how rural social enterprises engage with locational, institutional, material and identity aspects of their ‘place’, which indicates their ‘placial embeddedness’. Moreover, our findings also demonstrate how these organisations engage in, and combine market, redistribution and reciprocity relations, which indicates their ‘substantive hybridity’. Based on the interrelated nature of these findings, we argue that it is through a process of placial substantive hybridity that rural social enterprises foster social innovation in order to contribute to an integrated development of their localities. They harness and (re)valorise (untapped) local resources while complementing these with other resources from extra-local sources and accommodate and/or respond to structural-exogenous forces based on the needs of their local population in line with neo-endogenous rural development.

Social innovation (SI) research tends to understand rural SI processes as collective initiatives of equally engaged, supportive, and like-minded people. However, recent research has indicated that SI projects often benefit from dedicated key players who find support in their communities but also must deal with resistance and barriers. Against this backdrop, this article explores the figure of the key player in SI research and exposes the patterns of rural SI processes with the empirical cases of two small communities in Germany that jointly created rural infrastructure systems, thus seeking emancipation from remote political and economic control and marginalization. Using the empirical cases, we tested and specified a heuristic framework of key players that combines analytical dimensions at the micro-, meso-, and macro-levels. According to our findings, the heuristic framework is appropriate for analyzing key players in rural SI processes. Notably, we show that SI processes benefit from assertive key players and strong communities capable of joint action. Both are crucial for dealing with internal opposition and overcoming external hurdles that appear when novel approaches irritate familiar solutions and routines.

The phenomenon of rural marginalisation has intensified across the European Union after the 2008 global economic crisis, since the traditional state forms have been proved unable to promote an all-inclusive governing. Beyond-the-state governance systems are socially innovative, horizontal, networked, and collaborative institutional arrangements that are committed to breaking through this rural marginalisation. Marginalised rural areas provide a favourable context for socially innovative governance, since they contain small cohesive communities, while at the same time poor governing penetrates them. Nevertheless, social innovation in rural governance remains largely unexplored. The novel contribution of this article consists in underlying this research gap and exploring the question of how socially innovative governance is fostered or impeded within marginalised rural regions. Comparative research was conducted in between the marginalised rural regions of Muhlviertel in Austria, Baixo Alentejo in Portugal, and Phthiotis in Greece. The comparative results revealed some of the parameters that foster socially innovative governance; a decentralized government structure, advanced interregional networking, a discourse dissemination among the stakeholders and the civil society, as well as an organizational stability of the involved institutions for social innovation to become embedded. This article points to socially innovative governance that has a more codified and systematized hypostasis by recommendation of unique governance qualities.

A simple and accurate method based on degenerate four-wave mixing (FWM) to evaluate optical fiber dispersion and nonlinearity is presented. We investigated the continuous wave degenerate FWM characteristics of two photonic crystal fibers (PCFs) and a SMF28 fiber. Simply by measuring and analyzing the continuous wave degenerate FWM conversion efficiency as a function of the wavelength difference between pump and signal waves, fitting between experimental data and analytical FWM conversion efficiency expression is carried out to determine the dispersion, dispersion slope and nonlinear coefficient of the fiber. The fiber parameters simultaneously obtained from a single set of FWM measurement for Single Mode Fiber 28 (SMF28) are found to be agreement with the manufacturer specifications for this fiber, thus validating the PCF results and, more generally, the acceptable accuracy of the simple method proposed here.

У статті наведено рівняння для дослідження залежності напруженості електричного поля в біологічно активних точках від параметрів радіоімпульсного випромінювання. Отримані результати дозволили провести аналіз якісних властивостей процесу взаємодії радіоімпульсного випромінювання з біологічно активними точками. Було запропоновано біологічно активні точки розглядати як діелектричний резонатор, поміщений в плоску діелектричне середовище. Тому, якщо частота заповнення радіоімпульсу близька до власної частоти такого резонатора, то можливо резонансне збільшення напруженості електричного поля в біологічно активних точках. При проведенні численних розрахунків було прийнято, що сумарний потенціал на мембрані біологічних клітин не повинен перевищувати величину в 100 мВ. У процесі теоретичного аналізу розробленої моделі було встановлено, що для підвищення життєздатності новонароджених телят хворих диспепсією їх біологічно активні точки слід опромінювати інформаційним радіоімпульсного випромінюванням в діапазоні змін параметрів: тривалість імпульсів 0,5 · 10-7 ... 1,5 · 10-7 с; період проходження імпульсів 0,5 · 10 .... 1,5 · 10-5 с; величина щільності потоку потужності 15 мкВт / см2; частота заповнення імпульсів 50ГГц; час експозиції 15 ... 20 с; напруженість електричного поля E0 = 8,8 В / м, Еср = 5,7 В / м.

Introduction. To represent our national goods at the Western market it is necessary to ensure, first of all, the competitiveness of local products through complex mechanization of technological processes, labour costs reduction, increasing yields and quality of received products. The aim of the research is to provide the comparative analysis of technology and machines for potato harvesting, to determine the main factors that influence the agricultural technical indicators of harvesting equipment, to develop technological process patterns and to examine the strategies for improving the potato root-and-harvest machines. Methods and methodology. The research was carried out on the basis of technological and constructional analysis of technology and machines for potato harvesting. The methods of comparison and mathematical modelling of technological processes were used in the study. The research studies of Ukrainian and foreign scientists on technology and machines for potato harvesting were used as information base of our paper. Results. A comparative analysis of technology and machines for potato harvesting made it possible to determine the main factors that influence the agricultural performance of harvesting machinery and to develop the technological process for a digger, a double-loader digger and a harvester (harvesting machines). A theoretical model of the technological processes of harvesting machines includes both uncontrolled and controlled factors that influence the choice of a potato harvesting technology of a digger, a double-loader digger and a harvester. Nevertheless, the first factors are connected, primarily, with the conditions of potato growth and its biological properties, and the second factors deal with the organizational and technical measures, including the improvement of the working bodies of machines (cutting, dirt digging through, top separation, longitudinal hump with cut roller and other constructive and kinematic parameters), quality of received products, length and terms of potato storage, available farming human resources, free vehicle in the period of harvesting, farm potato storage, farm equipment for potato cleaning and sorting.

Unit square visibility graphs (USV) are described by axis-parallel visibility between unit squares placed in the plane. If the squares are required to be placed on integer grid coordinates, then USV become unit square grid visibility graphs (USGV), an alternative characterisation of the well-known rectilinear graphs. We extend known combinatorial results for USGV and we show that, in the weak case (i.e., visibilities do not necessarily translate into edges of the represented combinatorial graph), the area minimisation variant of their recognition problem is $${{\,\mathrm{{\textsf{N}}{\textsf{P}}}\,}}$$ N P -hard. We also provide combinatorial insights with respect to USV, and as our main result, we prove their recognition problem to be $${{\,\mathrm{{\textsf{N}}{\textsf{P}}}\,}}$$ N P -hard, which settles an open question.

Exciting recent advances in genome sequencing, coupled with greatly reduced storage and computation costs, make genomic testing increasingly accessible to individuals. Already today, one’s digitized DNA can be easily obtained from a sequencing lab and later used to conduct numerous tests by engaging with a testing facility. Due to the inherent sensitivity of genetic material and the often-proprietary nature of genomic tests, privacy is a natural and crucial issue. While genomic privacy received a great deal of attention within and outside the research community, genomic security has not been sufficiently studied. This is surprising since the usage of fake or altered genomes can have grave consequences, such as erroneous drug prescriptions and genetic test outcomes. Unfortunately, in the genomic domain, privacy and security (as often happens) are at odds with each other. In this article, we attempt to reconcile security with privacy in genomic testing by designing a novel technique for a secure and private genomic range query protocol between a genomic testing facility and an individual user. The proposed technique ensures authenticity and completeness of user-supplied genomic material while maintaining its privacy by releasing only the minimum thereof. To confirm its broad usability, we show how to apply the proposed technique to a previously proposed genomic private substring matching protocol. Experiments show that the proposed technique offers good performance and is quite practical. Furthermore, we generalize the genomic range query problem to sparse integer sets and discuss potential use cases.

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .

Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years. Further, the tree fruit architecture contributes to the light interception and improves tree growth, fruit quality, and fruit yield, in addition to easing the process of orchard management and harvest. Currently tree architectural traits are measured manually by researchers or growers, which is labor-intensive and time-consuming. In this study, the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders, physiologists and growers in collecting architectural traits efficiently and in a standardized manner. For this, a consumer-grade red–green–blue (RGB) camera was used to collect apple tree side-images, while an unmanned aerial vehicle (UAV) integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures. The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems (Spindle, V-trellis, Bi-axis), two rootstocks (‘WA 38 trees grafted on G41 and M9-Nic29) and two pruning methods (referred as bending and click pruning). The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model. The traits extracted from sensing data included box-counting fractal dimension (DBs), middle branch angle, number of branches, trunk basal diameter, and tree row volume (TRV). The results from ground-based sensing data indicated that there was a significant (P