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Topic modeling is a popular technique for exploring large document collections. It has proven useful for this task, but its application poses a number of challenges. First, the comparison of available algorithms is anything but simple, as researchers use many different datasets and criteria for their evaluation. A second challenge is the choice of a suitable metric for evaluating the calculated results. The metrics used so far provide a mixed picture, making it difficult to verify the accuracy of topic modeling outputs. Altogether, the choice of an appropriate algorithm and the evaluation of the results remain unresolved issues. Although many studies have reported promising performance by various topic models, prior research has not yet systematically investigated the validity of the outcomes in a comprehensive manner, that is, using more than a small number of the available algorithms and metrics. Consequently, our study has two main objectives. First, we compare all commonly used, non-application-specific topic modeling algorithms and assess their relative performance. The comparison is made against a known clustering and thus enables an unbiased evaluation of results. Our findings show a clear ranking of the algorithms in terms of accuracy. Secondly, we analyze the relationship between existing metrics and the known clustering, and thus objectively determine under what conditions these algorithms may be utilized effectively. This way, we enable readers to gain a deeper understanding of the performance of topic modeling techniques and the interplay of performance and evaluation metrics.

How does language friction affect alliance formation? Language friction is a form of cultural friction arising from structural differences in the respective languages used by potential partners to reason and solve problems together. A little language friction may prompt partners to rethink solutions, thereby enhancing collaboration, but excessive friction may impede collaboration. We develop a Language Friction Index (LFI) to quantify relative differences in linguistic structure for any language pair. Utilizing a unique data set of semiconductor design activities (1988-2001), our empirical analysis finds an inverted U-shaped relationship between partners' LFI and the likelihood of cross-border research and development (R&D) alliance formation. This relationship is further moderated by prior ties and technological distance. Our findings have several important implications, including: (1) language differences are a measurable and discernible source of cultural friction; (2) the effects of language friction are economically significant and strategically consequential; (3) certain aspects of language friction occur independent of language proficiency and persist despite the use of lingua franco to reduce language barriers; (4) linguistic diversity is an indirect marker of cognitive diversity, which is useful in boosting creativity, especially in first-time collaborations; (5) beyond R&D alliances, language friction may also influence other types of strategic interactions and organizational processes.

Prior scholarship generally examines the returns generated by firm-government engagement. These studies are based on an implicit and understudied assumption – the firm’s strategic choice of whether to engage with the government. Here, we unpack the drivers of this choice. To do so, we construct a population-level sample of U.S. high-tech ventures founded between 2015–2017; the full sample exceeds one million firms. We then utilize government records to identify initial firm-government engagement; approximately 24,000 high-tech ventures reveal this preference by firm age three. We examine a range of external and internal factors that may motivate such a choice. The results indicate that firm-government engagement most prominently coincides with firm resource constraints. Features driving such engagement include: (i) underrepresented minority-owned firms; (ii) small firms; (iii) firms with greater early-stage growth potential; and (iv) firms located in less intensive entrepreneurial settings. This study offers managerial, policy, and scholarly contributions by uncovering new insights around firm strategy and government opportunities for high-tech ventures.

Phosphoric acid and commercial fertilizers contain a significant quantity of fluoride, which comes from the phosphoric acid mineral, rock phosphate. HF and SiF4 vapours are two of the most significant emissions from the phosphoric acid industry. The main source of emissions is the reactor where H2SO4 combines with phosphate rock. Not only gaseous emissions but also solid emissions in the form of precipitates and insoluble fluoride bearing liquid compounds contribute to the emissions. Besides the solid, liquid and gaseous emissions that are generated from factories during the production of PA, if there is residual fluoride in the PA that is used to make phosphate fertilizers, the concentration of fluoride in the soil is likely to increase both from factory emissions and also from application of phosphate fertilizers downstream. According to recent studies, the fluoride content in a few commercial fertilizers is nearly 1% by weight, which is a concerning figure. Fluoride levels in jowar grains and soil were reported to be 1.4 ppm and 2.6 ppm, respectively, in a few areas in Maharashtra, India, which is again due to the use of fertilizers. Fluoride concentrations as high as 6 ppm have been observed in a few tea samples from the USA and Ireland. As a result, it's critical to separate fluoride from phosphoric acid and use the separated fluoride in the production of industrially important compounds. Many industries avoid fluorine removal because the products obtained post removal have no or very little market value. Hence, efficient fluoride removal and effective utilization technologies are the need of the hour. The paper describes the methods for fluorine separation and its effective utilization.

Around the world, governments make substantial investments in public sector research and development (R&D) entities and activities to generate major scientific and technical advances that may catalyze long-term economic growth. Institutions ranging from the Chinese Academy of Sciences to the French National Centre for Scientific Research to the Helmholtz Association of German Research Centers conduct basic and applied R&D to create commercially valuable knowledge that supports the innovation goals of their respective government sponsors. Globally, the single largest public sector R&D sponsor is the U.S. federal government. In 2019 alone, the U.S. government allocated over $14.9 billion to federally funded research and development centers (FFRDCs), also known as national labs. However, little is known about how federal agencies’ utilization of FFRDCs, their modes of R&D collaboration, and their adoption of non-patent intellectual property (IP) policies (copyright protection and materials transfer agreements) affect agency-level performance in technology transfer. In particular, the lack of standardized metrics for quantitatively evaluating government entities’ effectiveness in managing innovation is a critical unresolved issue. We address this issue by conducting exploratory empirical analyses of federal agencies’ innovation management activities using both supply-side (filing ratio, transfer rate, and licensing success rate) and demand-side (licensing income and portfolio exclusivity) outcome metrics. We find economically significant effects of external R&D collaborations and non-patent IP policies on the technology transfer performance of 10 major federal executive branch agencies (fiscal years 1999–2016). We discuss the scholarly, managerial, and policy implications for ongoing and future evaluations of technology transfer at federal labs. We offer new insights and guidance on how critical differences in federal agencies’ interpretation and implementation of their R&D management practices in pursuit of their respective missions affect their technology transfer performance outcomes. We generalize key findings to address the broader innovation processes of public sector R&D entities worldwide.

Remote patient monitoring (RPM) enables clinicians to maintain and adjust their patients’ plan of care by using remotely gathered data, such as vital signs, to proactively make medical decisions about a patient’s care. RPM interventions have been touted as a means to improve patient care and well-being while reducing costs and resource needs within the health care ecosystem. However, multiple interworking components must be successfully implemented for an RPM intervention to yield the desired outcomes, and the design and key driver of each component can vary depending on the medical context. This viewpoint and perspective paper presents a 4-component RPM infrastructure framework based on a synthesis of existing literature and practice related to RPM. Specifically, these components are identified and considered: (1) data collection, (2) data transmission and storage, (3) data analysis, and (4) information presentation. Interaction points to consider between components include transmission, interoperability, accessibility, workflow integration, and transparency. Within each of the 4 components, questions affecting research and practice emerge that can affect the outcomes of RPM interventions. This framework provides a holistic perspective of the technologies involved in RPM interventions and how these core elements interact to provide an appropriate infrastructure for deploying RPM in health systems. Further, it provides a common vocabulary to compare and contrast RPM solutions across health contexts and may stimulate new research and intervention opportunities.

Research and development (R&D) consortia are specialized strategic alliances that shape the direction and scope of firm innovation activities. Little research exists on the performance consequences of participating in R& D consortia. We study the effect of patent pools, a unique form of R& D consortia, on firm performance in innovation. While prior research on alliances generally implies that patent pools enhance firm innovation, our study finds the opposite. Analyzing data on systemic innovation in the global optical disc industry, we find that patent pool formation substantially and significantly decreases both the quantity and quality of patents subsequently generated by licensors and licensees relative to the patenting activity of nonparticipants. Our empirical findings suggest that patent pools actually inhibit, rather than enhance, systemic innovation by participating firms.

Cheese whey effluent (CWE) is a byproduct from cheese making industry having a high (50,000–100,000 mg/L) chemical oxygen demand (COD) which makes it mandatory to treat this effluent. Unlike the traditional methods known for CWE treatment – fermentation, enzymatic hydrolysis, ultrafiltration, etc., aqueous-phase reforming (APR) is a technology which helps to treat the wastewater by valorising it producing high heating value gases such as hydrogen (H 2 ), thus achieving a double benefit. In this work, APR of lactose as a model compound from CWE was carried out using a Pt promoted Ni/Htlc catalyst (where Htlc refers to hydrotalcite) in a stirred batch reactor. Experimental trials were performed where the reaction parameters viz. temperature (488–518 K), catalyst loading (2–6 kg/m 3 ), reaction time (1.5–6 h) and lactose concentration (1–5 wt%) were optimized. For optimized parameters, H 2 selectivity of 73% was achieved. The catalyst support Htlc was prepared using Cu and Al, the former being water gas shift (WGS) promoter. Furthermore, the effect of promotion by Pt was investigated with three different loadings (1–5%), where 2.5% Pt outperformed others. The Ni loading was fixed at 10% in all the catalysts. The synthesized catalyst was characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET) analysis and Fourier transform infrared spectroscopy (FTIR). Finally, based on the experimental data, a rate law was proposed where the rate constant and adsorption constant of lactose were determined using multilinear regression. This work provides a proof-of-concept investigation for valorising CWE via APR using a novel catalyst Pt-Ni/Htlc.

Domestic sewage containing low concentrations of oxygenated compounds can be effectively converted into H 2 and other gaseous fractions via catalytic aqueous phase reforming (APR). Starch, glucose, meat peptone and sunflower oil were chosen as model compounds for carbohydrates, proteins and lipids respectively. Trials were performed in a stirred batch reactor and reaction parameters, viz. catalyst loading (0–10 kg/m 3 ), temperature (180–235 °C), pH (5–9) and reaction time (1.5–6 h) were optimized. A commercial 5% Pt/C catalyst was employed for the study. Another noble metal catalyst Ru/C was also investigated but Pt/C was found to be superior. Along with H 2 production, Chemical Oxygen Demand (COD) reduction of sewage was studied. For optimum reaction conditions, H 2 production was 2.13 mmoles/COD g,i (where, COD g,i refers to initial COD in gram) and 60% COD reduction was observed. A process integrating hydrothermal carbonization followed by APR of the hydrothermal liquor was investigated to check for its effect on the H 2 yield. Electron microscopy, powder X-ray diffraction, X-ray photoelectron spectroscopy, Fourier Transform Infrared spectroscopy, H 2 chemisorption, NH 3 Temperature Programmed Desorption and BET analysis were used to investigate several aspects of the Pt/C catalyst. Kinetic parameters like Turnover Frequency (TOF) and apparent activation energy (E a ) were determined. E a was estimated to be 19.5 kJ/mol. This work reported an application of APR to effectively utilize the wastewater for producing valuable gases along with the treatment of wastewater making it suitable for industrial use. Graphical abstract

Transferring power from one rotational axis to another in numbers of axes requires portable equipment such as gear. In operation, involuted teeth were reported as bending and pitting failures. Bending fatigue causes gear tooth cracks at the root, and pitting causes more wear. This bending failure was reduced with the help of material selection, geometric modifications like changes in profile shape, transmission error, module, tip relief, pressure angles, and the application of forces. A normal force acting on a gear tooth gets resolved into three components, like tangential, radial, and axial. When it comes to enhancing bending strength, the geometric forms of profiles have a significant impact. A variety of tools and techniques, including hobbing, shaping, grinding, etc., are available at the fingertips for altering gearing tooth. Changing the borders of gear tooth profiles with hobbing tools is becoming more popular among the authors as a means for altering gear tooth. There are four distinct geometric profiles that may be used to change gear geometries and enhance bending strength by reducing stress on the tooth. These profiles include trochoidal, circular, bezier, and cubic spines, and they are implemented using a hob cutter. It is noteworthy to note that these designs boost strength by integrating the shape of number of teeth on the spur gear. This study compares the number of teeth on the gear’s pinion with other geometric characteristics, including pressure angle, module, etc., in order to discover the optimal way for modifying the involute tooth of the gear in order to increase the bending resistance effectively of the spur gear. For the purpose of boosting the strength of gear material, a number of research endeavours have utilized circular profiles; nonetheless, this profile is older than the Bezier curve profile. Here, Bezier curves with a three-and five-point profile give better results than other profiles with significant reductions in bending stress for the same quantity of tooth on the gear.