Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery of Cobalt, Nickel and Copper Compounds from UHT Processed Spent Lithium-ion Batteries by Hydrometallurgical Process

ABSTRACT Lithium-ion batteries (LIBs) are employed in various electronic devices and industries. The recovery of valuable metals from spent LIBs has attracted much interest due to potential environmental issues and economic values. Metallic alloys can be obtained from the UHT process of spent LIBs. In this study, a hydrometallurgical process was developed for the recovery of metal compounds from synthetic metallic alloys containing Co, Ni, and Cu. This process consisted of leaching, solvent extraction, stripping and precipitation. First, the metallic mixtures were completely leached by the mixture of 2.0 moL·L−1 HCl and 10%(v/v) H2O2 solutions at 60°C. Metal ions in the leachate were sequentially separated by solvent extraction with 0.7 moL·L−1 Cyanex301 and 1.0 moL·L−1 Aliquat 336 for Cu(II) and 1.0 moL·L−1 ALi-SCN for Co(II), whereas Ni(II) was remained in the final raffinate. The compounds of Cu(II) and Co(II) were recovered from the respective loaded organics by precipitation stripping method, while Ni(II) compounds were recovered by precipitation from the final raffinate without changing its acidity. Mass balance of the whole process indicated that the recovery percentage of metal compounds such as CuC2O4, CoCO3, and NiC2O4 · 2 H2O was 98.6, 99.4, 99.8% from the leaching solution and the purity of the compounds was higher than 99.9%. A process was proposed for the recovery of pure metal compounds from spent LIBs. The advantages of this process are the possibility that metal compounds with high purity can effectively be synthesized and the aqueous raffinates and the stripped organic solutions after each step can be recycled.
Invasive Species Control: Predation on the Alien Crab Percnon gibbesi (H. Milne Edwards, 1853) (Malacostraca: Percnidae) by the Rock Goby, Gobius paganellus Linnaeus, 1758 (Actinopterygii: Gobiidae)

Invasive alien species (IAS) are one of the greatest causes of native species extinction. Indeed, they represent a global threat for biodiversity and can also affect the economy and human health. The colonization success of IAS is presumably not only due to their biological and ecological characteristics, but also to the lack of predators and/or parasites in the invaded new areas. In the present work, we demonstrate evidence of predation of the invasive alien crab Percnon gibbesi (H. Milne Edwards, 1853) by the Rock Goby Gobius paganellus Linnaeus, 1758. The diet of G. paganellus was studied analyzing the stomach content of 162 specimens collected in the central Mediterranean Sea. The results obtained from the calculation of the diet indices, namely, frequency of occurrence (%F), percentage weight (%W), percentage abundance (%N), and the Index of Relative Importance (%IRI), showed that small benthic crustaceans were the main prey types. Additionally, these indices and the Levins’ index (Bi) clearly indicated that the invasive crab P. gibbesi was by far the most abundant prey type in the diet of G. paganellus. The relevance of this predator–prey interaction and the role of native species for the biological control of invasive ones are discussed. We also provide a general view on the diet of G. paganellus and other biological and ecological aspects of specimens studied from the central Mediterranean Sea.
Determination of Optimum Combination of Voxel Size and b-value for Brain Diffusion Tensor Imaging

Optimum combination of voxel size resolution and b-value for whole brain imaging has been determined. Data images were acquired using a 1.5T magnetic resonance imaging (MRI) system (GE Signa HDxt). Diffusion tensor imaging (DTI) scan was performed on phantom and a human volunteer. Six protocols which consist of various combination of voxel size and b-value were evaluated. Measurement of signal-to-noise ratio (SNR) and DTI parameter indices were carried out for both phantom and in-vivo studies. Due consideration was given to a combination of parameters yielding sufficient SNR with DTI values comparable to those obtained from previous reported studies. For the phantom study, SNR ≥ 20 was found in all of the protocols except for a combination of voxel size of 2.0 × 2.0 × 2.0 mm3 with b-value of 1200 s/mm2 (V2.0 B1200) and that of voxel size of 2.0 × 2.0 × 2.0 mm3 with b-value of 1000 s/mm2 (V2.0 B1000). For in-vivo study, all protocols presented SNR > 20. It was found that a combination of voxel size of 2.5 × 2.5 × 2.5 mm3 with b-value of 1000 s/mm2 (V2.5 B1000) and that of voxel size of 2.5 × 2.5 × 2.5 mm3 with b-value of 700 s/mm2 (V2.5 B700) displayed the most comparable ADC and FA values with references. In terms of anatomic coverage, V2.5 B700 was found better than V2.5 B1000 as it assures coverage of the whole brain. In conclusion, a combination of voxel size of 2.5 × 2.5 × 2.5 mm3 with b-value of 700 s/mm2 was considered as optimum parameters for brain DTI.
Implementation barriers of smart technology in Indian sustainable warehouse by using a Delphi-ISM-ANP approach

PurposeIn the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and distribution of various organizations have started adopting smart technologies globally. However, the adoption of smart technologies in the Indian warehousing industry is minimal. The study aims to identify the implementation barriers of smart technology in the Indian warehouse to achieve sustainability.Design/methodology/approachThis study employs an integrated Delphi-ISM-ANP research approach. The study uses the Delphi approach to finalize the barriers identified from the detailed literature review and expert opinion. The finalized 17 barriers are modeled using interpretive structural modeling (ISM) to get the contextual relationship. The ISM method's output and analysis using the analytical network process (ANP) illustrate priorities.FindingsThe study's findings showed that the lack of government support, lack of vision and mission and the lack of skilled manpower are the most significant barriers restricting the organization from implementing smart and sustainable supply chain practices in the warehouse.Practical implicationsThis study would help the practitioners enable the sustainable warehousing system or convert the existing warehouse into a smart and sustainable warehouse by developing an appropriate strategy. This study would also help reduce the impact of different barriers that would strengthen the chance of technology adoption in the warehouses.Originality/valueThe literature related to adopting smart and sustainable practices in the warehouse is scarce. Modeling of adoption barrier for smart and sustainable warehouse using an integrated research approach is the uniqueness of this study that have added value in the existing scientific knowledge.
The Herd Behavior on Peer-To-Peer Online Lending Markets: Evidence from China

Based on the transaction data and related borrowers’ characteristics of Renrendai.com, this study conducts an empirical study on the influencing factors of the investor’s herd behavior and rationality of herd behavior on a Chinese online lending platform. We mainly find that investors’ herd behavior exists significantly on Renrendai.com; there is an “inverted U-shaped” relationship between the number of bids and the herd behavior of investors. When the number of bids exceeds a certain amount, the time required for the order to obtain another bid will be prolonged, and the investors’ herd behavior will be slowed down; herd behavior on Renrendai.com in Chinese market is a partly rational pursuit, but irrational in general.
Physical Characteristics of Unstructured Coronal Clouds

Active regions in the inner solar corona, when observed in X-ray emission, consist of bright, hot loops surrounded by unstructured clouds. The emission from the clouds extends to a height of ≈4–5 × 104 km at temperatures of ≈2–3 MK. These “hot clouds” are variable, but persist for many days and do not appear to connect directly to the active region streamers or other large-scale structures observed higher in the corona. We present an observational analysis of these diffuse structures to establish basic plasma parameters such as magnetic field strength, particle density, and temperature. The values of β, the ratio of the plasma pressure to the magnetic field pressure, were found to be generally less than unity, though often approaching unity in the upper portions of the active region, where the hot clouds are located. The magnetic field may therefore only partially confine these regions and inhibit flare-like instabilities that could otherwise be driven by gradients of plasma pressure and current density.
The effect of using theodolite 3D AR in teaching measurement error on learning outcomes and satisfaction of civil engineering students with different spatial ability

ABSTRACT The purpose of this study was to investigate the effect of augmented reality (AR) using a 3D app in a smartphone on students’ learning outcomes and satisfaction in teaching angle measurement error to vocational high school students with different spatial ability. A quasi-experimental pretest/posttest was employed. There were 197 students from three vocational high schools in the southern, northern, and eastern part of Taiwan participated in this study. To minimize the biased result, we established one control group and one experimental group in each school. The experimental group used multimedia teaching tools and AR technology as a supplement, while the control group was taught by multimedia teaching tools only. We assessed some factors that possibly affect the learning outcomes and learning satisfaction, including different spatial abilities and different digital materials as teaching tools. Our finding highlights that different teaching materials influence students’ learning outcomes and satisfaction, whereas spatial ability does not affect. Further discussion and implementation are also identified in this study.
The Public Health Effects of Legalizing Marijuana

Thirty-six states have legalized medical marijuana and 18 states have legalized the use of marijuana for recreational purposes. In this paper, we review the literature on the public health consequences of legalizing marijuana, focusing on studies that have appeared in economics journals as well as leading public policy, public health, and medical journals. Among the outcomes considered are: youth marijuana use, alcohol consumption, the abuse of prescription opioids, traffic fatalities, and crime. For some of these outcomes, there is a near consensus in the literature regarding the effects of medical marijuana laws (MMLs). As an example, leveraging geographic and temporal variation in MMLs, researchers have produced little credible evidence to suggest that legalization promotes marijuana use among teenagers. Likewise, there is convincing evidence that young adults consume less alcohol when medical marijuana is legalized. For other public health outcomes such as mortality involving prescription opioids, the effect of legalizing medical marijuana has proven more difficult to gauge and, as a consequence, we are less comfortable drawing firm conclusions. Finally, it is not yet clear how legalizing marijuana for recreational purposes will affect these and other important public health outcomes. We will be able to draw stronger conclusions when more posttreatment data are collected in states that have recently legalized recreational marijuana. (JEL I12, I18, K32, K42, R41)
Evolution of research related to fruit and vegetable waste in agrifood supply chains: analysing past publications seeking to identify a path to future research

Population growth, coupled with a growing demand for food, and its consequent waste and economic, social, environmental, and nutritional impacts, has drawn attention to global discussions related to such issues. In this context, the objective of this research was to provide an overview of studies addressing food waste, in particular, fruits and vegetables. To achieve this purpose, a systematic literature review was carried out, covering papers published in international journals from 2007 to 2017, available in the Science Direct database. The results point out the best practices to avoid food waste, as mentioned in the literature considered. In addition, this study gathered information regarding the main characteristics of publications, such as the evolution of the number of publications per year; main methods and techniques used in research, and which journals excel at covering the theme. This paper may be useful to researchers and practitioners interested in this topic since it systematizes the knowledge related to food waste in fruit and vegetable supply chains.
Ablation and thermal insulation properties of silicone rubber-polyarylacetylene-carbonwoven laminates for solid rocket motor

Silicone rubber-polyarylacetylene-carbon woven laminates (SRWL) with two-dimensional structure were prepared using needled technique. The ablation and insulation mechanism of SRWL were investigated by oxyacetylene flame system, equilibrium swelling technique, thermogravimetry (TG), scanning electron microscopy (SEM) and X-ray diffraction (XRD). Results showed that the molecular weight between the crosslinks of the interlaminar complex is reduced with the increasing of the content of polyarylacetylene (PA). SRWL with PA has superior anti-ablation properties and high residue compared with that of without PA. With the increasing of PA content, the linear ablation rate of SRWL first decreases and then increases. When the content of PA is 10 phr, the linear ablation rate is 0.0276 mm s−1 and the back-face temperature peak is 71 °C. SRWL filled with 10 phr concentration of PA show comparably improved ablation properties and PA hinders the thermal transmission because of forming of compactly integrated self-protection refractory layer, ceramic compounds such as SiC, Si and some endothermic behaviour.
0099 Validity of ICD-10-CM diagnosis codes for traumatic brain injury in VA administrative data

to analyze the data using Atlas t.i. Coding will be done independently and intercoder reliability will be assessed. Results Re-occurring themes in the data analysis will inform the significance of social supports and what social supports are available to this population. Data collection is underway and preliminary study results will be available by April 2021. Conclusions This research will inform what healthy relationships are supporting long-term recovery and appropriate parenting practices that could prevent child injury and abuse as well as increase permanency outcomes.
Investigation of the confinement effects on the blast wave propagated from gas mixture detonation utilizing the CESE method with finite rate chemistry model

ABSTRACT In this paper, we investigate the effect of confinement on the explosion characteristic of a certain volume of a gas mixture in an environment. Instead of using the equivalent weight of TNT, we use a computational fluid dynamics method with a reduced chemical kinetic mechanism to simulate the gas mixture detonation. In this method, reacting flow equations including realistic finite-rate chemistry models are solved by employing the space-time conservation element and solution element (CESE). To evaluate the accuracy of the kinetic mechanism and the validity of the simulation of the volumetric explosions, including the blast wave propagation and its interaction with the walls, the explosion of a certain volume of stoichiometric propane/oxygen mixture in a semi-confined volume is investigated and the numerical results are compared with an experimental data. The excellent agreement between the numerical results and the experimental data indicates that it is possible to predict the behavior of the blast wave generated by the detonation of a gas mixture in a relatively complex environment accurately by using appropriate chemical kinetics. Therefore, we have used this method to investigate the effects of confinement on the blast wave propagation caused by gas detonation in more detail.
Reframing a Novel Decentralized Knowledge Management Concept as a Desirable Vision: As We May Realize the Memex

Proposing a major (though envisaged synergetic) shift in the knowledge management (KM) paradigm needs to convince a skeptical audience. This article attempts such a feat and motivates its conceptual considerations by fusing a wide scope of theoretical KM-related foundations in response to current KM unsustainabilities and emerging enabling technologies. The envisioned workflows, infrastructure, affordances, and impact resulting from the progressing design science research and prototyping efforts are consolidated and reframed, guided by a five-step visioneering process and twelve triple-criteria-clusters combining innovative, technological, and vision-related qualities. Inspired by Bush’s “Memex”, a desirable vision never realized since its suggestion three quarters of a century ago, the novel KM system (KMS) pursues the scenario of a mutually beneficial co-evolution between individual and institutional KM activities. This article follows up on the unsatisfactory and unsustainable state of current KM affairs suffering from accelerating information abundance, invisible work, structural interdisciplinary holes, lacking personal tools, and widening opportunity divides. By portraying a potentially transformative and game-changing technology, the crafting and drafting of a desirable, sustainable, and viable KMS vision assures transparency and can be more easily shared with a critical mass of stakeholders as a prerequisite for creating the respective future KM reality. The drafting of the “Desirable Sustainability Vision” is envisaged to assist a currently accepted KMS start-up project and investment.
Software‐defined networking in vehicular networks: A survey

Software‐defined networking (SDN) is a new paradigm that consists in decoupling the control plane from the data plane, which offers the flexibility of configuring and managing networks. Recently, an extension of SDN to vehicular ad hoc networks (VANETs), named software‐defined vehicular networks (SDVN), has been emerged to expand the capabilities of VANET in terms of the provided services and to overcome the VANET issues. Also, SDN has gained a lot of interest to be involved in the extensions of VANET, which are vehicular cloud and vehicular fog. In this paper, we overview the use of SDN in VANET. We survey its deployment in the classic vehicular networks, 5G‐VANET, vehicular cloud, and vehicular fog. Thereafter, we discuss some case studies of SDN in the vehicular context. Then, we present a taxonomy of the existing architectures followed by the major challenges in the implementation of SDN in the vehicular context. Next, we review the tools used to simulate SDN‐based VANET scenarios. Finally, we give some future research directions.
Revisiting the Miles and Snow typology of organizational strategy: uncovering interrelationships between strategic decision-making and public organizational performance

ABSTRACT This study aims to reexamine the Miles and Snow strategy stance framework by uncovering the interrelationships between strategy content, strategic decision-making, and public organizational performance. The data for this study were collected from 224 respondents in senior management across public organizations in the Turkish Republic of North Cyprus (TRNC). Structural equation modeling (SEM) was used to analyze the data collected. The study found that the most important predictor of the three types of Miles and Snow typologies of strategy stances to be the prospector one followed by the reactor stance, while defenders have no significant effect on the use of performance information for both strategy implementation and reporting decision-making alongside public organizational performance. Our empirical analysis examines the question of whether the strategy content approaches adopted by public organizations in the TRNC and the use of performance information for strategic decision-making are conducive to enhanced and improved public organizational performance.
A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.
Recyclable, weldable, mechanically durable, and programmable liquid metal-elastomer composites

Liquid metal (LM)-based elastomers have received growing interest for wide ranges of applications spanning from soft robotics to flexible electronics. However, the fabrication of multifunctional LM-based elastomer, in particular, featuring recyclability and transience while simultaneously offering excellent mechanical performance and stability, remains a challenge. Herein, we report a strategy for the fabrication of durable and recyclable multifunctional LM elastomer composites, which consist of LM droplets as functional fillers and Diels–Alder (DA) bond crosslinked polyurethane (PU) networks as the polymer matrix. The composite shows good mechanical properties and can be spatially tuned from an electrical insulator to a conductor through a so-called “mechano-training” process. The mechano-trained composite exhibits extraordinarily stable electrical performance even after 10 000 stretching-releasing cycles at 100% tensile strain. The DA bond-crosslinked network endows the composite with good self-healing property, high freedom in (re)programming of 3D shapes, and favorable transience. Moreover, spatially thermal and photothermal heating enables the repair/welding of broken circuits or programming of intricate shapes. Besides, the composite can be swiftly destructed by dissolution at an elevated temperature while the valuable LM can be retrieved at a yield of 89%. The multifunctionality, good mechanical and electrical performance, and in particular transience render this composite an environmentally friendly material for flexible transient electronics, actuators, and wearable devices.