Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact o f Hotel Employees’ Mental Health During Pandemic on The Level of Quality Service

Climate crisis is arguably the biggest challenge that the world is currently facing. Extreme weather events are becoming more frequent while the continuous rise in sea level is putting billions of lives will be at risk. Solving the climate crisis requires global carbon emission to peak by 2025, which means that carbon-intensive fossil fuels must be replaced by low-carbon renewable sources in fulfilling our energy needs. Solar energy, due to its abundance, rapidly decreasing cost, and low carbon emission, is one of the most promising energy sources. Many studies have calculated and compared the life cycle greenhouse gas (GHG) emissions of solar photovoltaic (PV) systems with conventional fuel generation. Nevertheless, the life cycle GHG emission values are affected by many location- and technology-dependent factors. This work provides an up-to-date and realistic assessment of the life cycle GHG emission for PV systems in Indonesia through thorough literature review and harmonization according to Indonesian parameters. The study shows that PV GHG emission of 37.3-64.3 gCO2eq/kWh are much lower than the current emission intensity of the Indonesian power sector. By fulfilling rising electricity demand using PV, up to 654 MtCO 2 eq can be avoided annually by 2050.
Monte Carlo Augmented Channel Estimator

– We have developed a novel Monte Carlo augmented channel estimator for millimeter-wave orthogonal frequency-division multiplexing systems to better estimate and correct a transmitted orthogonal frequency-division multiplexed signal by incorporating the predeﬁned known responses of system hardware and their errors. We compare our estimator against current channel estimators for different phased-array conﬁgurations and propagation channels. By accounting for these known responses of system hardware, our estimator provides an order-of-magnitude reduction of the standard deviation of the error vector magnitude and a reduction of the mean and standard deviation of the bit error ratio.
Ultra-Miniaturised CMOS Current Driver for Wireless Biphasic Intracortical Microstimulation

This work shows an ultra-miniaturised and ultralow-power CMOS current driver for biphasic intracortical microstimulation. The CMOS driver is composed of a leakage-based voltage-to-current converter and an H-bridge circuit providing biphasic charge-balanced current stimulation. The circuit has been simulated, fabricated and tested. The current driver consumes 1.87 µW with a supply voltage of 1.8 V, and it occupies a silicon area of 15×12.4 µm2. The driver works in linearity in the current range between 23−92 µA.
Examining country development indicators and e-waste under the moderating effect of country development levels and e-waste policy

PurposeThe purpose of this paper is to investigate if country development indicators, i.e. gross domestic product per capita (GDPPC), literacy rate, internet penetration and urban population, influence the generation of e-waste on a global level. The moderation effect due to differences between countries in terms of absence or presence of e-waste policy and level of development is also checked.Design/methodology/approachThis is an archival study that builds upon data from United Nations (UN), World Bank and Global E-waste Statistics Partnership. The authors did a path analysis comprising mediation and multigroup analyses to decipher the proposed rese arch model containing data from 172 countries.FindingsThe results indicate that GDPPC, literacy rate, internet penetration and urban population do not directly influence the generation of e-waste. However, higher internet penetration in developing countries leads to higher e-waste, while higher literacy rates in developed countries suppress e-waste generation. When it comes to e-waste policy, a higher urban population without a regulatory legal framework boosts higher e-waste. The authors observed that higher internet penetration leads to higher e-waste in the presence of e-waste policy as well.Originality/valueThis is the first study to include economic well-being indicators in elaborating e-waste generation, on a global scale. No previous study has observed differences between countries nested in e-waste policy and level of development.
Chapter 2: Stochastic Processes on Complex Networks

We now discuss the issue of modeling dynamic processes on complex networks. We start with a general presentation in terms of necessary mathematical foundations on stochastic modeling. Later, we provide specific examples to solidify these concepts. Finally, we conclude with a discussion about exact simulation techniques. In the following, each interacting component Xn, n = 1, 2, . , N , in a complex networked system assumes a state xn(t) ∈ N at each time point t ≥ 0, where N is the set of all natural numbers {0, 1, 2, . }. The N × 1 state vector x(t) completely characterizes the system at time t. Here, x(t) evolves with time, but not deterministically. Hence, if we run multiple simulations of the same system from identical initial conditions, we cannot expect to obtain the same dynamics in each simulation. Therefore, {x(t), t ≥ 0} is a multivariate stochastic process, since it is a random vector whose values change as a function of time [i.e., x(t) is a random vector for any t]. To distinguish between a random vector and its realizations, we use capital letters, such as X , to denote random vectors and small letters, such as x, to denote realizations. For determining the statistical properties of a stochastic process {X (t), t ≥ 0}, we need to specify the joint probabilities p(x1, t1;x2, t2; .;xq, tq) := Pr[X (t1) = x1,X (t2) = x2, . ,X (tq) = xq], for every 0 ≤ t1 < t2 < · · · < tq, x1,x2, . ,xq, and q. However, for our applications we are mainly interested in calculating the probability π(x, t) =
Stimulus: Accelerate Data Management for Scientific AI applications in HPC

Modern scientific workflows couple simulations with AI-powered analytics by frequently exchanging data to accelerate time-to-science to reduce the complexity of the simulation planes. However, this data exchange is limited in performance and portability due to a lack of support for scientific data formats in AI frameworks. We need a cohesive mechanism to effectively integrate at scale complex scientific data formats such as HDF5, PnetCDF, ADIOS2, GNCF, and Silo into popular AI frameworks such as TensorFlow, PyTorch, and Caffe. To this end, we designed Stimulus, a data management library for ingesting scientific data effectively into the popular AI frameworks. We utilize the StimOps functions along with StimPack abstraction to enable the integration of scientific data formats with any AI framework. The evaluations show that Stimulus outperforms several large-scale applications with different use-cases such as Cosmic Tagger (consuming HDF5 dataset in PyTorch), Distributed FFN (consuming HDF5 dataset in TensorFlow), and CosmoFlow (converting HDF5 into TFRecord and then consuming that in TensorFlow) by 5.3 x, 2.9 x, and 1.9 x respectively with ideal I/O scalability up to 768 GPUs on the Summit supercomputer. Through Stimulus, we can portably extend existing popular AI frameworks to cohesively support any complex scientific data format and efficiently scale the applications on large-scale supercomputers.
Development of a Benchmark Corpus to Support Entity Recognition in Job Descriptions

We present the development of a benchmark suite consisting of an annotation schema, training corpus and baseline model for Entity Recognition (ER) in job descriptions, published under a Creative Commons license. This was created to address the distinct lack of resources available to the community for the extraction of salient entities, such as skills, from job descriptions. The dataset contains 18.6k entities comprising five types (Skill, Qualification, Experience, Occupation, and Domain). We include a benchmark CRF-based ER model which achieves an F1 score of 0.59. Through the establishment of a standard definition of entities and training/testing corpus, the suite is designed as a foundation for future work on tasks such as the development of job recommender systems.
The effects of rapid results initiative as a performance management tool-a case study of Ministry of Environment and Mineral Resources

The study examined the effects of Rapid results Initiative as a performance management tool A case study of Ministry of Environment and Mineral Resources. Rapid results Initiatives was embraced by the Government of Kenya as a structured methodology for building and practicing result base management that is required to add impetus to the implementation of Economic Recovery Strategy and National Development. The study focused on the Effects of RRI Initiatives undertaken by the MEMR since its roll out in Government in the year 2004. An in-depth interview schedule was used and the researcher had to identify five key informants who were involved in the launch and implementation of RRI in their Divisions/Departments. From the study findings, it was established that despite the marked improvement in service delivery during the periods the initiatives were on, the Ministry did not have the mechanism of its regular uptake. All the respondents agreed that there has not been a comprehensive and thorough grounding of staff in RRI operational mechanisms like the setting of smart targets, work planning and monitoring and evaluation techniques. However the study also established that RRI implementation in the Ministry has brought on positive changes like enhanced achievement of performance targets, change from “Business as Usual” attitude to results orientation and team work and accountability for results.
NLINE C ONSUMER B EHAVIOR IN S ERBIA D URING THE C RISIS C AUSED BY C OVID -19

In parallel with the development of the digital market, a new generation of consumers has emerged. The digital market has become an integral part of the lives of modern consumers and it is simply difficult to imagine middle and younger consumers not being present in the digital market on a daily basis to gather information important for purchasing decisions and / or purchasing. There is a growing need for new knowledge, theories and models that indicate consumer behavior on the Internet, due to the evolution of online shopping, which is becoming a vital aspect of customer relations and the creation of marketing strategies. The corona virus pandemic that occurred in March 2020 is not just a health crisis, it has changed the way people around the world live. This paper examines whether there are changes in the level of online consumption and changes in consumer behavior in Serbia in general.
Shining Light on Calcium-Mediated Morphogenesis : Forward Engineering Organ Development with Optogenetics

Cells communicate to coordinate cellular processes across tissues, and Ca 2+ -ions are second messengers facilitating such multiscale coordination. However, current understanding of the biological mechanisms precludes direct control of Ca 2+ mediated processes. Here, we have adopted a forward engineering approach to understand the role of Ca 2+ -signaling in morphogenesis using optogenetics. This is a novel method of regulating Ca 2+ -signaling in epithelial tissues. The output of Ca 2+ -signaling in terms of tissue morphogenesis can thus be controlled by adjusting width-half-max of optogenetic activation. Broader implications of this work include organism and organ-based drug screening assays and disease treatments connected to dysregulation of Ca 2+ -signaling dynamics.
Towards UAV-assisted monitoring of onshore geological CO2 storage site

Scientists all over the world look for solutions to reduce greenhouse gas emissions in an effort to achieve proclaimed emissions reduction targets. An intriguing candidate with the potential to make a substantial contribution to this attempt is carbon capture and storage (CCS). The key advantage of CCS is that it provides the possibility to make a significant impact on the reduction of anthropogenic carbon dioxide (CO2) emissions from power plants and carbon-rich industry processes while maintaining existing fossil fuel energy infrastructure. The technique could therefore be used as a transitional solution until fossil fuels can be eliminated from the energy generation mix, and the energy efficiency of industrial processes as well as appliances and products is further improved. Like other technologies, CCS comes with its risks and rewards. To minimize possible negative impacts on humans as well as on the environment, it is necessary to understand the risks and to address them accordingly. A range of monitoring solutions for geological CO2 storage sites is available. However, a cost-effective solution for the regular observation of atmospheric CO2 concentrations (or tracer concentrations) of large areas above onshore geological CO2 storage sites has yet to be developed. This thesis discusses the use of a helicopter unmanned aerial vehicle (UAV) to fill this gap. The robot platform and its autopilot are designed to cope with ongoing sensor developments in addition to providing safety features necessary for the beyond line-of-sight operation of the UAV. The design focuses on the use of commercial off-the-shelf components for the aerial platform in order to shorten the development time and to reduce costs. The autopilot does neither enforce a specific helicopter model nor defines a set position estimation unit to be used. Access to the control loop enables low-level extensions like obstacle avoidance to be implemented. The developed solution allows the monitoring of an area of approximately 750 m2 with one set of batteries in one altitude with a spatial resolution of 2 m by 2 m. Experiments show that point source leaks of as low as 100 kg CO2 per day can be detected and their source located. As opposed to autonomous take-offs of the helicopter UAV, autonomous landings on small dedicated helipads require an accurate localization system. A time difference of arrival (TDOA) based acoustic localization system which is based on planar microphone arrays with at least four microphones is proposed. The system can be embedded into the landing platform and provides the accuracy necessary to land the UAV on a helipad of the size of 1 m by 1 m. A review of existing TDOA-based approaches is given. Simulations show that the developed approach outperforms its direct competitors for the targeted task. Furthermore, experimental results with the developed UAV confirm the feasibility
Finding the memory engram

Since the time of Plato and Aristotle (circa 350 BCE), memory is believed to be stored as enduring changes in the brain. However, the first scientific articulation of this concept only emerged in the 20th century when Richard Semon introduced the term “engram” to describe the neural substrate for storing and recalling memories. Since then, we have made great progress in studying engrams, to the point that we are able to visualize and manipulate them. Here, I will talk about new technologies that allowed us to tag and manipulate memory at the level of individual neurons.
A cc ep te d A rti cl e Rab 24 interacts with the Rab 7 / RILP complex to regulate endosomal degradation

Endocytosis is a multistep process engaged in extracellular molecules internalization. Several proteins including the Rab GTPases family coordinate the endocytic pathway. The small GTPase Rab7 is present in late endosome (LE) compartments being a marker of endosome maturation. The Rab interacting lysosomal protein (RILP) is a downstream effector of Rab7 that recruits the functional dynein/dynactin motor complex to late compartments. In the present study, we have found Rab24 as a component of the endosome-lysosome degradative pathway. Rab24 is an atypical protein of the Rab GTPase family, which has been attributed a function in vesicle trafficking and autophagosome maturation. Using a model of transiently expressed proteins in K562 cells, we found that Rab24 co-localizes in vesicular structures labeled with Rab7 and LAMP1. Moreover, using a dominant negative mutant of Rab24 or a siRNA-Rab24 we showed that the distribution of Rab7 in vesicles depends on a functional Rab24 to allow DQ-BSA protein degradation. Additionally, by immunoprecipitation and pull down assays, we have demonstrated that Rab24 interacts with Rab7 and RILP. Interestingly, overexpression of the Vps41 subunit from the This article is protected by copyright. All rights reserved. This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/tra.12431 A cc ep te d A rti cl e homotypic fusion and protein-sorting (HOPS) complex hampered the co-localization of Rab24 with RILP or with the lysosomal GTPase Arl8b, suggesting that Vps41 would affect the Rab24/RILP association. In summary, our data strongly support the hypothesis that Rab24 forms a complex with Rab7 and RILP on the membranes of late compartments. Our work provides new insights into the molecular function of Rab24 in the last steps of the endosomal degradative pathway.
Geomechanical evaluation of macro-block caving options using 3D numerical modelling at Chuquicamata underground project in Chile

The Chuquicamata underground project in the Atacama Desert in northern Chile is one of the largest planned mining projects in the world to use the block caving method with macro-blocks options, to mine copper ore. VCP-Codelco (Vice-President Office of the National Copper Corporation of Chile) is currently completing a prefeasibility engineering study of this project, which considers the construction and operation of at least two macro-block mining units to be managed independently from each other. A geomechanical study has been carried out to analyse various options regarding to pillar sizes and mining sequences for the macro-blocks caving configurations considered for the project. As part of this study, complex 3D continuum models have been developed to assess the effects of the above options on the mechanical response of the underground openings, with emphasis in stress concentration in certain critical areas of the excavations such as macro-blocks pillars and rib-pillars. Relevant geological features such as major faults and lithological units were also considered in the models. This paper describes the main aspects of the Chuquicamata underground project by focusing on the 3D numerical models that were performed to evaluate the macro-block extraction options for this block cave.
LiDAR-as-Camera for End-to-End Driving

The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving commands, e.g., steering angle, as output. However, simulation studies have shown that depth-sensing can make the end-to-end driving task easier. On a real car, combining depth and visual information can be challenging due to the difficulty of obtaining good spatial and temporal alignment of the sensors. To alleviate alignment problems, Ouster LiDARs can output surround-view LiDAR images with depth, intensity, and ambient radiation channels. These measurements originate from the same sensor, rendering them perfectly aligned in time and space. The main goal of our study is to investigate how useful such images are as inputs to a self-driving neural network. We demonstrate that such LiDAR images are sufficient for the real-car road-following task. Models using these images as input perform at least as well as camera-based models in the tested conditions. Moreover, LiDAR images are less sensitive to weather conditions and lead to better generalization. In a secondary research direction, we reveal that the temporal smoothness of off-policy prediction sequences correlates with the actual on-policy driving ability equally well as the commonly used mean absolute error.
Randomized Coordinate Subgradient Method for Nonsmooth Optimization

Nonsmooth optimization finds wide applications in many engineering applications. In this work, we propose the Randomized Coordinate Subgradient method (RCS) for solving nonsmooth convex and nonsmooth nonconvex (nonsmooth weakly convex) optimization problems. RCS randomly selects one block coordinate to update at each iteration, making it more practical than updating all coordinates. We consider the linearly bounded subgradients assumption for the objective function, which is more general than the traditional Lipschitz continuity assumption, to account for practical scenarios. We then conduct thorough convergence analysis for RCS in both convex and nonconvex cases based on this generalized Lipschitz-type assumption. Specifically, we establish the Õ(1/ √ k) convergence rate in expectation and the õ(1/ √ k) almost sure asymptotic convergence rate in terms of suboptimality gap when f is nonsmooth convex. If f further satisfies the global quadratic growth condition, the improvedO(1/k) rate is shown in terms of the squared distance to the optimal solution set. For the case when f is nonsmooth weakly convex and its subdifferential satisfies the global metric subregularity property, we derive the O(1/T ) iteration complexity in expectation, where T is the total number of iterations. We also establish an asymptotic convergence result. To justify the global metric subregularity property utilized in the analysis, we establish this error bound condition for the concrete (real valued) robust phase retrieval problem, which is of independent interest. We provide a convergence lemma and the relationship between the global metric subregularity properties of a weakly convex function and its Moreau envelope, which are also of independent interest. Finally, we conduct several experiments to demonstrate the possible superiority of RCS over the subgradient method. ∗D. Zhu is also with School of Data Science, The Chinese University of Hong Kong, Shenzhen. L. Zhao and D. Zhu were partially supported by the National Natural Science Foundation of China (NSFC) under Grant No. 71871140. X. Li was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No. 12201534 and by Shenzhen Science and Technology Program under Grant No. RCBS20210609103708017. ar X iv :2 20 6. 14 98 1v 2 [ m at h. O C ] 1 7 A pr 2 02 3 Randomized Coordinate Subgradient Method for Nonsmooth Optimization A PREPRINT
Discovery of nanographene for hydrogen storage solving low reversibility issues

We found good reversibility of hydrogen uptake-release in vacancy-centered hexagonal armchair nanographene (VANG) based on density functional theory calculation. VANG has triply hydrogenated vacancy (V 111 ) at the center acts as a self-catalytic property to reduce an activation barrier of hydrogen uptake-release. We found remarkable features in an almost equal value of the activation energy barrier of 1.19 eV for hydrogen uptake and 1.25 eV for hydrogen release on V 111 of VANG. The dehydrogenation showed slightly exothermic and the hydrogenation became slightly endothermic, suggesting the eﬃciency of hydrogen uptake-release. In high hydrogen coverage, the quintuply hydrogenated vacancy (V 221 ) is formed with some hydrogenated located in the in-plane and armchair edges. This structure produces an exothermic hydrogen release from the in-plane with an energy barrier of not more than 2 eV. This ﬁnding potentially addresses the low reversibility issues in the organic chemical hydrides as hydrogen storage materials.