Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Ozone Treatment on the Properties of Oil Palm Empty Fruit Bunch Sulfonated Chemi-Mechanical Pulp

Oil palm empty fruit bunch (EFB) as an abundant waste material can be utilized for pulp production to alleviate the shortage of raw materials in the paper industry. Sulfonated chemi-mechanical pulp (SCMP) has great potential in paper making industry. However, its poor performance due to the high surface lignin content limits its application. In this study, we used EFB as raw material to produce SCMP and systematically studied the effect of ozone treatment on pulp properties. Results show that the surface structure and morphology of fibers exhibited distinct differences under different ozone dosage treatments. Compared to the control, the content of surface lignin of pulps was reduced by 2.56%, 4.64%, 13.24% and 25.24% when ozone consumption was 1, 3, 5 and 7 wt%, respectively. Meanwhile, the treated pulp had a lower drainability at the same refining energy level. Moreover, the physical and optical properties of handsheets were improved significantly after ozone treatment. Ozone treatment is a very efficient way to improve the performance of SCMP. Additionally, this method avoids complicated processes and chemical consumption. Therefore, as an effective, environmentally friendly and low-cost treatment method, ozone treatment can improve the performance of EFB SCMP and thus provide a high-quality pulp resource.
Self-Assembly and Magnetic Order of Bi-Molecular 2D Spin Lattices of M(II,III) Phthalocyanines on Au(111)

Single layer low-dimensional materials are presently of emerging interest, including in the context of magnetism. In the present report, on-surface supramolecular architecturing was further developed and employed to create surface supported two-dimensional binary spin arrays on atomically clean non-magnetic Au(111). By chemical programming of the modules, different checkerboards were produced combining phthalocyanines containing metals of different oxidation and spin states, diamagnetic zinc, and a metal-free ‘spacer’. In an in-depth, spectro-microscopy and theoretical account, we correlate the structure and the magnetic properties of these tunable systems and discuss the emergence of 2D Kondo magnetism from the spin-bearing components and via the physico-chemical bonding to the underlying substrate. The contributions of the individual elements, as well as the role of the electronic surface state in the bottom substrate, are discussed, also looking towards further in-depth investigations.
Linking Overturning, Recirculation, and Melt in Glacial Fjords

Fjord circulation modulates the connection between marine‐terminating glaciers and the ocean currents offshore. These fjords exhibit both overturning and horizontal recirculations, which are driven by water mass transformation at the head of the fjord via subglacial discharge plumes and distributed meltwater plumes. However, little is known about how various fjord characteristics influence the interaction between 3D fjord circulation and glacial melt. In this study, high‐resolution numerical simulations of idealized glacial fjords demonstrate that recirculation strength controls melt, which feeds back on overturning and recirculation. The relationships between overturning, recirculation, and melt rate are well predicted by vorticity balance, reduced‐order melt parameterizations, and empirical scaling arguments. These theories allow us to take into account the near‐glacier horizontal velocities, which yield improved predictions of fjord overturning, recirculation, and glacial melt.
Potential of Dictyosphaerium sp. LC172264 Concomitant Remediation of Cassava Wastewater and Accumulation of Lipids for Biodiesel Production

As a way of making algal feedstock feasible for biofuel production, simultaneous utilization of microalga Dictyosphaerium sp. LC172264 for cassava wastewater remediation and accumulation of lipids for biodiesel production was investigated. The algal biomass, lipid contents and composition were measured from the autotrophic, heterotrophic and mixotrophic cultured algal cells. Physicochemical parameters of the cassava wastewater and bioremediation potentials were measured. Biodiesel properties were deduced and compared with the standards. The results showed that mixotrophic culture was the best for both biomass accumulation (1.022 g/L) and lipid contents (24.53%). Irrespective of the culture condition, the predominant fatty acids were similar and included 11-Octadecenoic acid (vaccenic acid (C19H36O2), oleic acid (C18H34O2) and 14-methyl pentadecanoic acid (isopalmitic acid (C17H34O2). The percentage reduction of total dissolved solids was 79.32% and 89.78% for heterotrophy and mixotrophy respectively. Biochemical oxygen demand was 72.95% and 89.35%, chemical oxygen demand was 72.19% and 84.03% whereas cyanide contents reduced from the initial value of 450 mg/L to 93.105 (79.31%) and 85.365 mg/L (81.03%) respectively. Dictyosphaerium sp. showed good growth and lipid production under mixotrophic condition and produced good quality biodiesel under the three cultivation modes. Even though both mixotrophic and heterotrophic conditions had good promise of cassava wastewater remediation by Dictyosphaerium sp. , mixotrophy showed superiority.
Feasibility of Awake Intravesical Botulinum Toxin Injection in Pediatric Neurogenic Bladder

Purpose: Cystoscopic injection of botulinum neurotoxin (BoNT) is typically performed under general anesthesia in pediatric patients with neurogenic bladder, accumulating anesthetic exposures and operating room costs. As most of these patients already tolerate clean intermittent catheterization (CIC), it has become our practice to offer a trial of awake injection. We report our initial experience here. We hypothesized that higher sensory level, female sex and absence of mental health issues or cognitive delay might predict successful first awake injection and decreased operative times. Materials and Methods: Surgical records from 2 academic hospitals from 2018–2020 were reviewed. Generalized linear models were fit to determine predictors of success and procedural length. Results: Trial of awake injection was offered to 22 patients. Eighteen patients (8 female, 10 male, 4–20 years old) elected to proceed. All 18 patients were managed with CIC at baseline, 14 had anxiety or behavioral issues, 10 had cognitive delay and 7 had sensory level below S2. Two patients (11%) required conversion to general anesthesia and one of these subsequently opted for a repeat awake injection trial. Fifteen of the 18 patients (83%) had or planned subsequent injections awake. Of the remaining, 1 proceeded to bladder augment, 1 is considering ileovesicostomy and 1 requested subsequent injections under anesthesia. No intraoperative complications occurred. Conclusions: Awake BoNT injection is feasible in pediatric patients with neurogenic bladder managed with CIC, even in the setting of intact sensation, well-managed mental health issues or cognitive delay, thereby increasing the viability of BoNT as an early tool in the management of neurogenic bladder.
Composite Experience Replay-Based Deep Reinforcement Learning With Application in Wind Farm Control

In this article, a deep reinforcement learning (RL)-based control approach with enhanced learning efficiency and effectiveness is proposed to address the wind farm control problem. Specifically, a novel composite experience replay (CER) strategy is designed and embedded in the deep deterministic policy gradient (DDPG) algorithm. CER provides a new sampling scheme that can mine the information of stored transitions in-depth by making a tradeoff between rewards and temporal difference (TD) errors. Modified importance-sampling weights are introduced to the training process of neural networks (NNs) to deal with the distribution mismatching problem induced by CER. Then, our CER-DDPG approach is applied to optimizing the total power production of wind farms. The main challenge of this control problem comes from the strong wake effects among wind turbines and the stochastic features of environments, rendering it intractable for conventional control approaches. A reward regularization process is designed along with the CER-DDPG, which employs an additional NN to handle the bias of rewards caused by the stochastic wind speeds. Tests with a dynamic wind farm simulator (WFSim) show that our method achieves higher rewards with less training costs than conventional deep RL-based control approaches, and it has the ability to increase the total power generation of wind farms with different specifications.
Deformation Mechanism and Surrounding Rock Control in High-Stress Soft Rock Roadway: A Case Study

Stability control for soft and broken surrounding rock of roadways is one important segment of mining support. Taking 1412 Roadway of a mine in Guizhou province as a research background, this paper studies the large deformation of surrounding rock and the failure of bolts and cables. The deformation and failure mechanism are analyzed by related theoretical analysis and field survey. Then, the feasibility of the composite controlling scheme, bolts and cables + grouting + steel tube concrete support, is verified by theoretical analysis, numerical simulation, and industrial test. Following results can be obtained: main reasons leading to the deformation of surrounding rock and the failure of cables and blots in the roadway are low strength and poor self-stability of surrounding rock, complex stress environment, low support resistance, and lack of reinforced support in crucial supporting sites; the control scheme can reduce the surrounding rock deformation by 40%, which meets the requirements of field application so that this practice can provide some guidance for other similar projects.
Warmer Winters Increase the Biomass of Phytoplankton in a Large Floodplain River

Winters are changing rapidly across the globe but the implications for aquatic productivity and food webs are not well understood. In addition, the degree to which winter dynamics in aquatic systems respond to large‐scale climate versus ecosystem‐level factors is unclear but important for understanding and managing potential changes. We used a unique winter data set from the Upper Mississippi River System to explore spatial and temporal patterns in phytoplankton biomass (chlorophyll a, CHL) and associated environmental covariates across 25 years and ∼1,500 river km. To assess the role of regional climate versus site‐specific drivers of winter CHL, we evaluated whether there were coherent long‐term CHL dynamics from north to south and across lotic‐lentic areas. We then estimated the degree to which these patterns were associated with climate variability (i.e., the Multivariate El Nino‐Southern Oscillation Index), winter severity (freezing degree days), river discharge, or site‐specific environmental variables (ice depth, snow depth, and nutrient concentrations). We found that winter CHL was typically highest in ice‐free reaches and backwater lakes, occasionally exceeding summer values. We did not find highly synchronous CHL dynamics across the basin, but instead show that temporal trends were independent among river reaches and lotic‐lentic areas of the river. Moreover, after accounting for these spatial dynamics, we found that CHL was most responsive to winter air temperature, being consistently higher in years with warmer winters across the basin. These results indicate that although productivity dynamics are highly dynamic within large river ecosystems, changes in the duration and severity of winter may uniformly increase wintertime productivity.
Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model

Sub‐seasonal streamflow forecasts are important for a range of water resource management applications, with a distinct practical interest in forecasts of high flows (e.g., for managing flood events) and low flows (e.g., for managing environmental flows). Despite this interest, differences in forecast performance for high and low flow events are not routinely investigated. Our study reveals that while forecasts evaluated over the full flow range can appear reliable, stratification into high/low flow ranges highlights significant under/over‐estimation of forecast uncertainty, respectively. We overcome this challenge by introducing a flow‐dependent (FD) nonparametric component into a post‐processing model of hydrological forecasting errors, the Multi‐Temporal Hydrological Residual Error (MuTHRE) model, yielding the MuTHRE‐FD model. The MuTHRE and MuTHRE‐FD models are compared in a case study with 11 Australian catchments, the GR4J rainfall‐runoff model and post‐processed rainfall forecasts from ACCESS‐S. Through its improved treatment of flow‐dependence, the MuTHRE‐FD model achieves practically significant improvements over the original MuTHRE model in the reliability of forecasted cumulative volumes for: (a) high flows out to 7 days; (b) low flows out to 2 days; and (c) mid flows for majority of lead times. The new MuTHRE‐FD model provides seamless sub‐seasonal forecasts with high quality performance for both high and low flows over a range of lead times. This improvement provides forecast users with increased confidence in using sub‐seasonal forecasts across a wide range of applications.
Retrieval and Calculation of Vertical Aerosol Mass Fluxes by a Coherent Doppler Lidar and a Sun Photometer

The direct and indirect radiation forcing of aerosol particles deeply affect the energy budget and the atmospheric chemical and physical processes. To retrieve the vertical aerosol mass fluxes and to investigate the vertical transport process of aerosol by a coherent Doppler lidar (CDL), a practical method for instrumental calibration and aerosol optical properties retrieval based on CDL and sun photometer synchronization observations has been developed. A conversion of aerosol optical properties to aerosol microphysical properties is achieved by applying a well-developed algorithm. Furthermore, combining the vertical velocity measured simultaneously with a CDL, we use the eddy covariance (EC) method to retrieve the vertical turbulent aerosol mass fluxes by a CDL and sun photometer with a spatial resolution of 15 m and a temporal resolution of 1 s throughout the planetary boundary layer (PBL). In this paper, we present a measurement case of 24-h continuous fluxes observations and analyze the diurnal variation of the vertical velocity, the aerosol backscatter coefficient at 1550 nm, the mean aerosol mass concentration, and the vertical aerosol mass fluxes on 13 April 2020. Finally, the main relative errors in aerosol mass flux retrieval, including sample error σF,S, aerosol optical properties retrieval error σF,R, and error introduced from aerosol microphysical properties retrieval algorithm σF,I, are evaluated. The sample error σF,S is the dominating error which increases with height except during 12:00–13:12 LST. The aerosol optical properties retrieval error σF,R is 21% and the error introduced from the aerosol microphysical properties retrieval algorithm σF,I is less than 50%.
Establishment of a Novel CD34+ Hematopoietic Stem Cell-Derived Xenograft Model of Hyperleukocytic Acute Myeloid Leukemia

Hyperleukocytic acute myeloid leukemia (HLL) with high early mortality is difficult to therapy. The related research on HLL is still in its infancy, and the establishment of a patient derived xenograft (PDX) model of HLL has not been fully reported. In this study, we establish a CD34+ hematopoietic stem cell-derived xenograft model of HLL for further exploration. Our findings demonstrated that the leukemia cells were detected in peripheral blood, bone marrow, liver, and spleen tissues in mice after inoculation of patient-derived cells. By performing a comparative analysis of multiple experiments, we found the irradiation doses, injection cell counts, gene abnormal and the survival status of the enrolled patients affected the survival time in mice. We successfully established a novel CD34+ hematopoietic stem cell-derived xenograft model of HLL, which shows great promise for mechanistic research, drug screening, individualized therapy, clinical efficacy assessment and precision medicine in HLL.
Developing System-Oriented Interventions and Policies to Reduce Car Dependency for Improved Population Health in Belfast: Study Protocol

Reducing car dependency requires orchestrated multi-sectoral, multi-policy action in a complex landscape. Thus, development of proposed interventions to reduce car dependency should be informed by systems thinking, complexity science, and socio-technical transition theory. We aim to co-design sustainable systems-oriented intervention approaches to reduce car dependency in Belfast. The study includes seven integrated tasks—1: Map stakeholders and partnerships influencing car dependency using stakeholder network analysis; 2: A review of systematic reviews regarding interventions to reduce car dependency; 3: Map-related policies via analysis of policy documents and semi-structured interviews; 4: A participatory group model building workshop to co-produce a shared understanding of the complex system perpetuating car dependency and a transition vision; 5: Using Discrete Choice Experiments, survey road users to evaluate the importance of transport infrastructure attributes on car dependency and on alternative modes of travel; 6: Citizen juries will ‘sense-check’ possible actions; and, 7: Stakeholders will interpret the findings, plan orchestrated multi-sectoral action, and agree on ways to sustain collaborations towards the common vision of reducing car dependency. We expect to attain a systemic view of the car dependency issue, potential intervention approaches to reduce it, and a framework for their integration through the co-ordination of stakeholder actions.
Social Dimensions in CPS & IoT Based Automated Production Systems

Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap between humans and systems in which human was seen as an important source of errors and disruptions. Today, the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised awareness about the central role humans have to play in manufacturing systems, the design of which must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of Things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different human social dimensions associated with CPS and IoT and focuses on their conceptual evolution regarding automated production systems’ sociability, notably by bringing humans back in the loop. Hereby, this paper aims to take stock of current research trends to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. Consequently, different models of sociability as a way to integrate humans in the broad sense and/or the develop future automated production systems have been identified from the literature and analysed.
Why are consumers following social media influencers on Instagram? Exploration of consumers’ motives for following influencers and the role of materialism

Abstract Despite the ubiquity of social media influencers (SMIs) and the clear value they hold for marketers, little is understood about the sociopsychological motives that drive consumers to follow them. The current research identified unique consumer motivations for following SMIs on Instagram and examined its association with important consumer behaviour outcomes (i.e. trust towards SMIs’ brand-related posts and frequency of purchasing SMI-recommended brands) as well as materialism. Based on survey data, findings revealed four motivations for following influencers on Instagram – authenticity, consumerism, creative inspiration, and envy – which had varying effects on trust and purchase frequency. Additionally, materialism was a significant individual difference variable that was strongly associated with the four motives, some of which served as key mediators underlying materialism’s impact on purchase behaviour. Managerial and theoretical implications for marketers and advertisers are discussed as well as suggestions for future research in this burgeoning area.
Effects of Expansionary Monetary Policy on Agricultural Commodities Market

Agricultural commodities experienced a rise in prices during the first decade of the 2000s. The literature shows that the monetary policies adopted by developed economies can influence practically all economic indicators of developing markets. This paper aims to evaluate the effect of selected monetary policy measures on the prices of three selected agricultural commodities: soy, corn and sugar. Secondly, the study analyzes the price formation of these commodities during a period of expansionary monetary policy, in order to better understand how they are influenced by unconventional instruments. The central hypothesis is that the excessive liquidity created by the FED spills over to emerging economies, boosting investment and consumption there and, lastly, causing a commodity cycle. Our data (January 2000–December 2019) support this hypothesis and prove that expansionary monetary policy is capable of impacting agricultural commodities’ prices, but by different channels, due to the specificities of each commodity. The fact that people have more capital due to the credit obtained from loans seems to influence the price of sugar; soy is highly influenced by exchange rates of emerging markets, and corn is not very responsive to the used variables, which might be due to the high production rates of this commodity in the U.S. and the protectionist policies adopted by the government.
A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking

This paper extends the results recently proposed in Part I of this research work focused on the stabilization of power electronic converters. This second part is devoted to cases in which the underlying control problems can be translated into tracking control problems. This is the case for DC-AC converters whose output must track a sinusoidal reference signal. The idea is to tackle the problem in a unified manner in order to avoid as much as possible the use of approximations and to exploit all the mathematical properties of the corresponding switched models. The case in which measurable or non-measurable perturbations are present is considered. The proposed techniques are illustrated for two particular DC-AC converters simulated using the PSIM software.