Search

Abstract The Octalysis framework, which systematically organizes gamified elements in non-game settings, such as training, has become one of the most famous persuasive tools for changing user behaviour. The field of Gamification Octalysis Framework research is expanding rapidly, moving beyond elementary questions like “what” and “why” to more complex ones like “how” and “when”. However, there remain empirical and theoretical challenges such as demonstrating the effectiveness of the Gamification Octalysis Framework and codifying the principles underpinning effective gamification design. This paper uses bibliometric analysis and scientific mapping to characterize the structure and development of the academic field of the Gamification Octalysis Framework, with the help of intellectual, conceptual, and social network structures of the framework. Our findings provide insight into the field’s research frontiers and intellectual structures, the interconnections between articles, authors, and keywords, the current collaborative networks, the hottest themes, and the most cited authors, publications, and sources, for instance, that in recent years, the Gamification Octalysis Framework has attracted significant research attention in the training field. Interestingly, 66.6% of cutting-edge research is done in the social sciences, as compared to science and technology. Researchers, academicians and professionals should be made aware of Octalysis framework gamified method because it has been shown to increase user engagement and motivation outside of social sciences game setting. We recommend that countries outside Europe integrate the Gamification Octalysis Framework in training into their learning programs and launch corresponding research centres and journals.

The Biak tsunami event on February 17, 1996, was triggered by a Mw 8.2 earthquake at 5:59 UTC (14:59 local time). Based on the field survey, the maximum tsunami height was not located on the coast that directly faces the earthquake epicenter. The maximum tsunami of up to 7.7 m was recorded at Farusi village on the opposite coast. In addition to the high tsunami hit, the fast arrival time in this village was an anomaly that raised questions regarding the multiple tsunami sources. Previous studies suspected a landslide when a rupture occurred, but no one had yet identified the dimensions and mechanism of the landslide. The purpose of this research is to increase understanding of tsunami generators and answer that question. The COMCOT software is used to perform tsunami simulations, integrating fault and landslide sources simultaneously. This study obtains the Biak tsunami generator from a fault source model with a length of 272 km, a width of 110 km, an average dislocation of 8 m, and a maximum slip of 10.6 m. Also, there are three landslides occurred in the south coast. One of the major landslide source model has dimensions length and width of 5.629 km and 14.595 km, respectively, and a thickness of landslide material of 50 m, with an average slope of the slip plane of 10° located in the Ramardori. These two source models answer the particular questions of the Biak tsunami incident. [ABSTRACT FROM AUTHOR]

Traffic control through video/image processing is a trending research topic within Intelligent Transportation Systems. The number of moving vehicles, the density of vehicles at the junctions, traffic anomalies, and traffic flow directly influence real-time traffic congestion. The primary goal of this work is to design an architecture that is simple and fast enough to use in real-time heterogeneous traffic scenes. The proposed methodology uses user-defined zones for determining vehicle occupancy and count based on traffic surveillance video. The angular integral projection function accompanies the background subtraction method for better localization. Traffic anomaly detection is implemented by incorporating intelligent sensor technology. Also, the skipping of frames at fixed intervals has increased the processing speed with minimal data loss. The compatibility with any Internet Protocol camera further distinguishes this approach from other state-of-the-art methods. The proposed algorithm has been tested on a publicly available traffic surveillance video dataset and real-time surveillance feeds. Experimental results from the real-time intersection scenes show an overall accuracy of 97.14% and an average processing speed of 92.91%. The accuracy of the detection module in the custom-made dataset is 98.73%. [ABSTRACT FROM AUTHOR]

The construction and assessment of an effective, cost-efficient anode catalyst for methanol oxidation reactions (MOR) are crucial in direct methanol fuel cells (DFMC) applications. This work unveils a quick, simple, yet effective method for synthesizing poly(aniline-co-pyrrole) using the "chemical oxidative polymerization" technique and creates an affordable and stable anode catalyst for DMFC applications. The chemical oxidative polymerization approach was employed to copolymerize aniline and pyrrole with incorporation of nickel, and the electrocatalyst showcased remarkable activity towards methanol oxidation reaction (MOR). Physiochemical characterizations such as scanning electron microscopy, FT-infrared spectroscopy, Raman spectroscopy, and thermogravimetric analysis were performed to validate the incorporation of transition metal nickel into the copolymerized conducting polymers (aniline-co-pyrrole), abbreviated as PANiPPy. Electrochemical techniques like cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy (EIS) were used to investigate the capability of the electrocatalysts to oxidize methanol in alkaline conditions. The electrode material NiPANiPPy displayed a higher oxidation current density 280.60 mA/cm2 from the cyclic voltammogram of the electrocatalysts in 1 M KOH + 1 M methanol compared to the other synthesised electrocatalysts since nickel provides a low onset potential while the copolymers PANiPPy provide good conductivity and mechanical stability. Moreover, NiPANiPPy exhibits the lowest current rate loss and a substantially higher current density among them after 3600 s with the highest current density of 100 mAcm−2. NiPANiPPy was also observed to have the lowest solution resistance with 0.8 Ω compared to other electrocatalysts in EIS spectra. Thus, the synthesized electrocatalysts has potential applications as an enhanced anode electrode for DMFC. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures" published in the journal Multimedia Tools & Applications. The correction notice highlights several errors in the original publication, including missing reference citations in figure captions, incorrect images in figures, and an incorrect paragraph in section 5. The correction also mentions that the original article has been corrected. The publisher, Springer Nature, maintains a neutral stance regarding jurisdictional claims and institutional affiliations. The authors of the article are Shaik Hasane Ahammad, R. Jayaraj, S. Shibu, V. Sujatha, Ch Prathima, L. Megalan Leo, R. Thandaiah Prabu, Md.Amzad Hossain, and Ahmed Nabih Zaki Rashed. [Extracted from the article]

In the medical field, ankylosing spondylitis (AS) is arthritis with symptoms that differs from person to person and takes a long time to evaluate. For predicting radiographic progression, the prediction within the prognostics employing the approach of time-series records performed reasonably well when used with clinical variables from the first visit dataset. The integration and analysis of numerous variables of different types had limitations as per prior research work under statistical analysis on the radiographic progressions. With the time-series approach propagated through the records fed via electronic means, the study has been developed utilizing machine learning models (ML) for radiographic progression estimation among patients impacted towards AS. These models' performance might be enhanced by adding more data, including radiography of the spinal column or even the lifetime data. Comparison has been made within Model A/diagnostic B's precision through the development of clinical model gaining the reach of 2.5% subjected to spondylarthritis characteristics listed in the categorization criteria attributed to Spondylarthritis Assessment under International Society. Furthermore, the abridged model with linear regression gained a reach of 2.6% for viability with a lower range Model of A/B. Therefore, Model A/B has achieved superior development clinically within the model for the prediction of prognostics of patients affected by AS; its use may help with the early detection and diagnosis of AS. [ABSTRACT FROM AUTHOR]

Groundwater is a critical resource for sustaining agriculture, industry, and human life. However, overexploitation and contamination of groundwater resources have led to significant environmental and socio-economic challenges. To address these challenges, there is a need for effective groundwater management strategies that utilize advanced technologies such as the Internet of Things (IoT) and machine learning algorithms. The research proposes an IoT-enabled groundwater monitoring system that utilizes K-Nearest Neighbors (k-NN) and Support Vector Machines (SVM) algorithms for sustainable water management. The system comprises IoT sensors that monitor groundwater levels, quality, and flow rates in real time, and transmit the data to a centralized server for analysis. The k-NN algorithm is used for initial data clustering, while SVM is used for classification and prediction tasks related to groundwater data. The system is designed to identify anomalies, detect trends, and provide insights into the usage and availability of groundwater resources. The proposed system can help in promoting sustainable water use practices, reducing water wastage, and optimizing groundwater usage. The research findings can be useful for water managers, policymakers, and other stakeholders in the agriculture and industrial sectors who are concerned with sustainable groundwater management. [ABSTRACT FROM AUTHOR]

The study of cell membrane structures under microgravity is crucial for understanding the inherent physiological and adaptive mechanisms relevant to overcoming challenges in human space travel and gaining deeper insight into the membrane-protein interactions at reduced gravity. However, the membrane dynamics under microgravity conditions is not unraveled yet. Moreover, the complexity of cells poses significant challenges when investigating the effects of microgravity on individual components, including cell membranes. Giant Unilamellar Vesicles (GUVs) serve as valuable cell-mimicking models and act as artificial cells, providing insights into the biophysics of membrane architecture. Herein, we have elucidated the membrane dynamics of artificial cells under simulated microgravity conditions. GUVs were synthesized in the size range of 20 ± 2.1 μm and their morphological changes were examined under simulated microgravity conditions using a random positioning machine. We observed that the well-defined spherical GUVs were transfigured and deformed into elongated structures under microgravity conditions. The membrane fluidity of GUVs increased sevenfold under microgravity conditions compared to GUVs under normal gravity conditions at 48 h. It is also noted that there is a reduction in the membrane microviscosity. The study sheds light on the membrane mechanics under microgravity conditions and contributes valuable insights to the broader understanding of membrane responses to microgravity and its implications for space exploration and biomedical applications. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) has necessitated the development of indoor photovoltaics to enable a web of self-powered wireless sensors/nodes. We analysed a CsPbI3 wide band gap perovskite for indoor photovoltaic application. An Indoor photovoltaic (IPV) device based on CsPbI3 showed a theoretical efficiency of 51.5% at a band gap of 1.8 eV under indoor light-emitting diode (LED) illumination. This high efficiency is due to better capture of the narrow emission spectrum of LED by a high band gap CsPbI3 absorber. Under low luminance of indoor light sources, there is a low density of photogenerated carriers, which increases the ratio of trapped electrons to photogenerated electrons. The low photogenerated carrier density lowered bulk recombination, and the high trapped electrons to photogenerated electrons ratio increases IPV sensitivity toward interfacial recombination. Finally, the device optimization strategies of the IPV device, distinct from outdoor illumination devices are highlighted. [ABSTRACT FROM AUTHOR]

Background: Poor oral health and oral dysbiosis were found to be associated with cancers, especially of the gastrointestinal (GI) system. But the cause-and-effect relationship and the effect of the risk are not yet known due to scarcity of literature. Understanding such risk relationship can contribute to an integrated multi-disciplinary approach for GI cancer prevention. Aim: The aim of the present systematic review and meta-analysis is to assess the role of oral dysbiosis on increasing the risk of digestive system cancers. Objective: To evaluate the effect of poor oral health on increasing the risk of gastrointestinal cancers. Methods: We conducted a systematic search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in databases PubMed, Elsevier, Wiley's online library and Web of Science from inception to February 2023 to include recent cohort studies that assessed the association between poor oral health and the risk of cancer. We assessed bias using the New Castle Ottawa scale. We used inferential statistics to describe the effect of oral dysbiosis on gastrointestinal cancers. We performed a sub-group analysis to assess the effect of oral conditions on individual cancers. Results: We included 10 longitudinal studies in the meta-analysis. The overall effect size of poor oral health and GI cancer risk was hazard's ratio (HR) =1.30 (95% CI: [1.14, 1.46]) (p<0.001) (I2 = 68.78). Sub-group analysis indicated that poor oral health increases the risk of esophageal cancer HR=1.61 (95% CI: [1.37, 1.85]), stomach cancer HR=1.33 (95% CI: [1.08, 1.58]), pancreatic cancer HR=1.90 (95% CI; [1.29, 2.50]) and colorectal and hepatocellular carcinoma HR=1.16 (95% CI: [1.08, 1.23]). Conclusion: The meta-analysis indicated that poor oral health was significantly associated with increasing the risk of GI cancers. [ABSTRACT FROM AUTHOR]

The aim of this research is to conduct an experiment that will evaluate the effect of combining of counter flow air-impinging arrangement and an arc-shaped turbulators over the absorber plate of solar heater. This combination is intended to improve the thermohydraulic performance of the solar air heater. The parameters such as energy transfer and frictional factor are studied at flow Reynolds number varies from 18,000 to 3600, and the influence of a hole jet bore ratio from 0.0430 to 0.109 is considered. At the optimal hole jet bore ratio of 0.0650, the thermo-hydraulic achievement parameter has a value of 1.75, which is the greatest possible value can be attained. When compared to multi-pass V and multiple V roughened solar air heaters based on the THAP, the current work is improved the performance by 55.3 and 32.9%, respectively. Such comparisons clearly demonstrate the superiority of the present work. Furthermore, a relationship is shown between the jet hole bore ratio and the thermohydraulic performance measure, with a maximum divergence of 10%. [ABSTRACT FROM AUTHOR]

In this work, CoWO4/’x’ g-C3N4 nanostructures were synthesized through a facile hydrothermal method. The structural, vibrational, and morphological properties of the prepared samples were characterized by powder XRD, FT-IR, and SEM analysis techniques. The results revealed the prepared sample possesses a monoclinic crystal structure and flower-like morphology without impurities. It has been shown that incorporating different quantities of g-C3N4 nanosheets into CoWO4 modifies its electrochemical characteristics by merging the redox activity of the metal tungstate host with the significant electrical conductivity of the g-C3N4 component. Among the prepared samples CoWO4/’x’ g-C3N4 (x = 1.0) electrode material has a high surface area, large electrical conductivity, and exhibited better electrode/electrolyte interaction. The electrochemical tests were performed on CoWO4/’x’ g-C3N4, (x composition = 0.5, 1.0, 2.0) where CWCN-10 exhibited a remarkable electrochemical performance with a high specific capacitance of 1092 Fg−1 at 5 mV/s with good stability over 5000 cycles. Hence, the proper optimization of carbon composite can effectively enhance the performance of metal tungstate as electrode material for supercapacitor application. [ABSTRACT FROM AUTHOR]

The present work deals with analyzing the effect of silicon dioxide on mechanical and thermal stability of hybrid polymer composite. Compression molding is adopted to develop the hybrid composite with constant 0.5 wt.% of graphene and varying wt.% of silicon dioxide (2, 4 & 6 wt.%). Modified hummers method is used to synthesis graphene by chemical exfoliation of graphene from graphite flakes. Mechanical properties of developed composite is investigated using tensile, flexural, impact and interlaminar shear testing follow by fracture surface analysis of tested samples. Thermal stability is examined by thermogravimetric analysis and functional group confirmation is carried out using Fourier transform infrared spectroscopy (FTIR). Investigation over mechanical properties of fabricated composite reveals that addition of silicon dioxide up to 4 wt.% depicted higher flexural and tensile strength. Increment in silicon dioxide percentage improves the toughness of composite. FTIR results confirms the presence of asymmetric stretching vibrations due the after effect of hybrid fillers. Thermogravimetric analysis results depict that incorporation silicon dioxide increases the activation of energy of matrix from ~ 24.2 to 31.5 kJ/mol. [ABSTRACT FROM AUTHOR]

A metal-free combination of rGO/g-C3N4-coupled SrTiO3 (SRN) ternary nanocomposite prepared via a wet impregnation method for UV–Vis light photocatalytic applications. Various physicochemical properties of the samples were investigated by several spectroscopic techniques including X-ray diffraction (XRD), FT-IR, Raman, field emission scanning electron microscopy with energy dispersive X-ray spectroscopy (FE-SEM-EDX), high-resolution transmission electron microscopy (HR-TEM), UV–Vis, photoluminescence (PL), X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) surface area analysis. The data suggest agglomerated SRT nanoparticles are dispersed and distributed throughout the surface of the rGO sheets and GCN nanostructures. The photocatalytic performance of the SRN towards combined mixed dye and its degradation activities were evaluated towards the most common industrial effluents, Rhodamine B (RhB) and Methylene blue (MB), under UV–Vis light illumination. The results revealed that the degradation efficiency of the SRN photocatalyst shows excellent performance compared with that of the binary composition and the pure SrTiO3 (SRT) sample. The reaction rate constant for RhB was estimated to be 0.0039 min−1 and for MB to be 0.0316 min−1, which are 3.26 (RhB) and 4.21 (MB) times faster than the pure SRT sample. The enhanced degradation efficiency was attained not only by interfacial formation but also by the speedy transportation of electrons across the heterojunction. After 5 runs of the photocatalytic recylic process, the SRN photocatalyst exhibited ultimate stability without structural changes, and no noticeable degradation was observed. The outcomes of the ternary SRN nanocomposite manifest a dual photocatalytic scheme, the photocatalytic enrichment could be caused by the Z-scheme charge transfer process between GCN, SRT, and rGO nanocomposite, which helps effectual charge separation and keeps a high redox potential. From the results, SRN sample provides insight into the integration of an effective and potential photocatalyst for wastewater treatment toward real-time environmental remediation applications. [ABSTRACT FROM AUTHOR]

All pests can be eliminated with the help of pesticides, which can be either natural or synthetic. Because of the excessive use of pesticides, it is harmful to both ecology and people's health. Pesticides are categorised according to several criteria: their chemical composition, method of action, effects, timing of use, source of manufacture, and formulations. Many aquatic animals, birds, and critters live in danger owing to hazardous pesticides. Metaldehyde is available in various forms and causes significant impact even when small amounts are ingested. Metaldehyde can harm wildlife, including dogs, cats, and birds. This review discusses pesticides, their types and potential environmental issues, and metaldehyde's long-term effects. In addition, it examines ways to eliminate metaldehyde from the aquatic ecosystem before concluding by anticipating how pesticides may affect society. The metal–organic framework and other biosorbents have been appropriately synthesized and subsequently represent the amazing removal of pesticides from effluent as an enhanced adsorbent, such as magnetic nano adsorbents. A revision of the risk assessment for metaldehyde residuals in aqueous sources is also attempted. [ABSTRACT FROM AUTHOR]

This study has clarified the optical fiber systems performance signature based on the dispersion mitigation techniques in based on the dispersion compensated dense wavelength division multiplexing (WDM) systems in the presence of RZ/NRZ modulation code method. The high-transmission reach-based dense multiplexing channels through the WDM network by OptiSystem software. The long reach dense multiplexing system is modeled at 100 GHz for high bit rates through SMF with possible 200 km transmission. The Dense WDM technique is tested with the input signal variations. Three compensation techniques are employed that are namely symmetrical dispersion compensated fiber, fiber Bragg grating method and Chirped fiber Bragg grating method. The study has demonstrated the efficient compensation methods for upgrading the optical/electrical signal per noise ratio especially with the use of chirped fiber Bragg grating technique. [ABSTRACT FROM AUTHOR]

Introduction: Hip osteoarthritis (OA) is one of the leading causes of disability and morbidity worldwide. It is estimated to affect 9.2% individuals globally with age over 45 years. Conventional treatment modalities have limitations and side-effects. To overcome these limitations, over the last decade, there has been an increased interest in the use of orthobiologics derived from autologous sources including platelet-rich plasma (PRP), bone-marrow aspirate concentrate (BMAC) and adipose tissue derived formulations. This review qualitatively presents the in-vitro, pre-clinical, clinical and on-going clinical studies exploring the safety and efficacy of BMAC for management of hip OA. Materials and methods: The electronic database search was done through PubMed, Embase, Web of Science, Scopus, ProQuest and Google Scholar till February 2024. The search terms used were "osteoarthritis" OR "hip osteoarthritis" OR "orthobiologics" OR "efficacy or use of orthobiologic treatment" OR "bone-marrow concentrate" OR "bone-marrow aspirate concentrate", AND "BMAC". The inclusion criteria were clinical studies of any level of evidence written in the English language, published till February 2024, evaluating the safety and efficacy of intra-articular administration of BMAC for the management of hip OA. Results: A total of 5 studies were included in this review for qualitative data synthesis. The total number of patients who participated in the study was 182, ranging from 4 to 112 in a single study. No adverse events were reported throughout the duration of the study. In addition, intra-articular administration of BMAC led to reduced pain, and improved function and overall quality of life (QoL). Conclusion: The results from this review demonstrated that administration of BMAC is safe and potentially efficacious in terms of reducing pain, improving function and overall QoL of patients with hip OA in short- and mid-term average follow-up based on the included studies. Nonetheless, more adequately powered, multi-center, prospective, double-blind, non-randomized and randomized controlled trials with long-term follow-up are warranted to establish long-term safety and efficacy of BMAC for management of hip OA and justify its routine clinical use. [ABSTRACT FROM AUTHOR]

High spatial resolution, accurate precipitation data, which is currently unavailable in many parts of the world, is required for a variety of applications. This study investigates the utility of employing Global Satellite Precipitation (GSP) in conjunction with ground-based precipitation data. Monthly studies of the research area were performed using fourteen various GSP models, including CHIRPS, CFS, CPC CMORPH, and others. According to our findings, CHIRPS is the most successful GSP model we investigated. All GSP models performed better when the daily precipitation data was adjusted using monthly mean linear adjustment factors. The approach consisted of five processes: feature class and feature selection, asset import into Google Earth Engine, GSP model evaluation, precision assessment of GSP and observation data, and regression analysis for spatial mapping. The results reveal that when adjusted for daily data, CHIRPS and other GSP models perform much better, showing that they are suitable for precipitation estimation. This work contributes to the development of methods for estimating precipitation, particularly in areas with limited ground-based data. The advice to use ground-based observations in addition to satellite-retrieved precipitation data is intended to improve weather forecast accuracy and aid early warning systems for severe weather. Our research stresses the importance of a hybrid strategy that combines high-resolution models and satellite data for effectively managing watersheds and disaster preparedness. [ABSTRACT FROM AUTHOR]

Aptamers are a class of molecular recognition elements that exhibit high binding affinity and specificity against their respective targets. In view of the many advantages aptamers harbor over their counterpart antibodies, we were impelled to isolate an RNA aptamer against progesterone receptor, particularly its DNA binding domain. A total of eight SELEX cycles were executed against the recombinant Progesterone Receptor DNA-binding domain (PR DBD). The RNA–protein complex in the gel shift assay was subjected to crush and soak method to elute the binders prior to conventional sequencing, the step of which was based upon to coin the term CRUSOAK-SELEX. The sequencing revealed three different classes of sequences, with one class termed, PRapt-3, showing the strongest binding against PR DBD. The dissociation constant of PRapt-3 RNA aptamer was estimated at 380 nM ± 35 nM. PRapt-3 was successfully used to develop aptamer-based diagnostic assays such as ELASA, aptamer-based dot blot, and aptamer-based western blot. The prominent highlight is the performance of the aptamer in aptacytostaining, which was unachievable with antibodies. Compared to its counterpart antibodies, PRapt-3 has a better penetration capacity in aptahistostaining using the formalin-fixed paraffin-embedded (FFPE) breast cancer cells and tissue blocks. This study represents the first ever demonstration of an aptamer against progesterone receptor and its diagnostic capacity. [ABSTRACT FROM AUTHOR]

Recent years have seen an increase in research on biodiesel, an environmentally benign and renewable fuel alternative for traditional fossil fuels. Biodiesel might become more cost-effective and competitive with diesel if a solid heterogeneous catalyst is used in its production. One way to make biodiesel more affordable and competitive with diesel is to employ a solid heterogeneous catalyst in its manufacturing. Based on X-ray diffraction (XRD) and Fourier Transform infrared spectroscopy (FTIR), the researchers in this study proved their hypothesis that iron oxide core–shell nanoparticles were generated during the green synthesis of iron-based nanoparticles (FeNPs) from Camellia Sinensis leaves. The fabrication of spherical iron nanoparticles was successfully confirmed using scanning electron microscopy (SEM). As a heterogeneous catalyst, the synthesised catalyst has shown potential in facilitating the conversion of algae oil into biodiesel. With the optimal parameters (0.5 weight percent catalytic load, 1:6 oil—methanol ratio, 60 °C reaction temperature, and 1 h and 30 min reaction duration), a 93.33% yield was attained. This may be due to its acid–base property, chemical stability, stronger metal support interaction. Furthermore, the catalyst was employed for transesterification reactions five times after regeneration with n-hexane washing followed by calcination at 650 °C for 3 h. [ABSTRACT FROM AUTHOR]

The primary objective of the present study is to investigate the mechanism of direct liquefaction of coal-related model compounds and to explore the influence of methanol as a solvent by using ReaxFF molecular dynamics simulation (MDS) software. A total of nine different coal-related model compounds viz. phenanthrene (PH), diphenylmethane (DM), naphthol (NP), phenol (PL), diphenyl ether (DP), dibenzyl ether (DB), dibenzothiophene (DBT), dibenzofuran (DF), and quinoline (QN) were considered to identify the complex reaction mechanisms, formation of intermediates, and the effects of methanol during the liquefaction process. For the simulation study, 8 molecules of a particular model compound were considered along with 80 methanol molecules in a 20 × 20 × 20 Å simulation box. The simulation studies were performed at 20 and 100 K/ps heating rates for the final temperature of 3000 K with a total simulation time of 150 ps duration. Methanol facilitated the breaking of C–O, C–S, C–N, and C–C bonds. Diphenylmethane led to a yield of 65.5 wt% of light tar (LT) at 20 K/ps, and the yield of light tar and heavy tar (HT) + char was maximum for DM at 65.5% and for DP at 43.10% for 20 K/ps. For 100 K/ps, the yield of LT and HT + char was maximum for DP at about 24.1% and for DM at about 69.39%. [ABSTRACT FROM AUTHOR]

Terahertz (THz) spectroscopy has become a potent tool for label-free, non-destructive chemical sensing, with applications that range from security screening to pharmaceutical investigation. To expand the possibilities of THz spectroscopy, it is essential to improve its sensitivity and selectivity. In this study, we present a brand-new optical fiber design that is specifically suited to take use of surface-enhanced spectroscopic features in the terahertz range: an octagonal photonic crystal fiber (PCF) with a square core. According to this analysis, a five-layer construction with square air openings for the core and cladding and an octagonal shape would be best. The mathematical study is accomplished using the PML's boundary condition with a finite element method (FEM) at the propagation of THz waves. The developed octagonal PCF sensor accomplishes exceptionally high relative sensitivity (90.65%, 91.93%, 93.06%) at 1.3 THz for three compounds, including water (n = 1.330), ethanol (n = 1.354), and benzene (n = 1.366), following the simulation method. In contrast, for the same three compounds at 1.3 THz, the low confinement loss (CL)'s are 2.12 × 10−13 dB/m, 2.00 × 10−13 dB/m, and 1.88 × 10−13 dB/m, respectively. In addition, we investigated the possible uses of this unique fiber in numerous industries, such as chemical sensing, environmental monitoring, and biomedical diagnostics. In terms of terahertz chemical sensing, the octagonal PCF with square core opens up previously untapped possibilities for the creation of highly sensitive and selective THz spectroscopic devices with significant societal implications. [ABSTRACT FROM AUTHOR]

This study focuses on the design and development of reduced graphene-oxide-anchored Co3O4 nanotubes. The graphene sheets were decorated with Co3O4 nanotubes using a simple one-pot, solution-phase process. Morphological examination revealed structures, such as nanotubes. The produced nanotubes had an average length of 1 μm, width of 312 nm, and diameter of 80 nm. The presence of Co, O, and C in the synthesized nanotubes was verified by EDAX analysis. The XRD diffraction pattern confirmed the cubic structure of the Co3O4 nanotubes. The Raman spectra indicate the existence of active modes in the obtained Co3O4. TGA confirmed that the synthesized rGO-Co3O4 was stable up to 850 °C. The pore size of the developed rGO/Co3O4 nanotubes was determined to be 3.539 nm, as observed using BET microscopy. The performance of the supercapacitor was tested in a 2 M KOH electrolyte after the rGO/Co3O4 nanocomposite electrode was fabricated. It shows pseudocapacitive performance and leads to a maximum specific capacitance (Csp) of 457 F/g in CV for a scan rate of 3 mV/s and 462 F/g and at a current density (CD) of 1 mA/g. [ABSTRACT FROM AUTHOR]

This study investigates the photocatalytic and antibacterial properties of zinc oxide nanoparticles (ZnO NPs) synthesized using Datura metel (D. Metel) leaf extract and characterized by XRD, UV–Vis, FTIR, and FESEM with EDX. The XRD analysis confirmed the hexagonal crystalline structure (average size 23.67 nm). UV–Vis analysis revealed a 3.25-eV direct band gap. FTIR spectrum exhibited strong absorption band at 539.03 cm−1 related to ZnO. The FESEM images exhibited presence of spherical ZnO nanoparticles. EDX spectrum illustrates the presence of Zn and O elements confirming the pristine nature of ZnO nanoparticles. In sunlight exposure of 80 and 100 min, ZnO NPs exhibited 98.29% and 86.65% degradation efficiency for methylene blue and methyl orange. Antibacterial tests demonstrated significant efficacy against Bacillus cereus, resulting in a 6-mm inhibition zone. This eco-friendly approach highlights ZnO NPs' potential in dye degradation and antibacterial applications. [ABSTRACT FROM AUTHOR]

This study investigated the use of nano-blended biodiesel, made by mixing waste transformer biodiesel with nanoscale aluminum oxide particles. A fuel blend ratio containing both diesel and biodiesel components was created with 100 parts per million of alumina nanoparticles. All tests were conducted at various loads in an engine, using diesel, biodiesel, and nano-blended biodiesel fuel. The outcomes demonstrated that adding nanoparticles improved engine thermal efficiency and reduced emissions, especially at higher fuel injection timings. The greatest increase in brake thermal efficiency, 6% higher than diesel mode, was achieved with a nano-blended biodiesel formulation containing 100 ppm of aluminum oxide, at a fuel injection timing of 27 before top dead center under full load conditions. With a constant nanoparticle-blended biodiesel content and fuel injection timing of 27 before top dead center, reductions of 1.5% in smoke, 33% in hydrocarbon, and 8% in carbon monoxide emissions were observed, with a 9.7% increase in NO emissions. [ABSTRACT FROM AUTHOR]

The syncytiotrophoblast is a multinucleated structure that arises from fusion of mononucleated cytotrophoblasts, to sheath the placental villi and regulate transport across the maternal–fetal interface. Here, we ask whether the dynamic mechanical forces that must arise during villous development might influence fusion, and explore this question using in vitro choriocarcinoma trophoblast models. We demonstrate that mechanical stress patterns arise around sites of localized fusion in cell monolayers, in patterns that match computational predictions of villous morphogenesis. We then externally apply these mechanical stress patterns to cell monolayers and demonstrate that equibiaxial compressive stresses (but not uniaxial or equibiaxial tensile stresses) enhance expression of the syndecan-1 and loss of E-cadherin as markers of fusion. These findings suggest that the mechanical stresses that contribute towards sculpting the placental villi may also impact fusion in the developing tissue. We then extend this concept towards 3D cultures and demonstrate that fusion can be enhanced by applying low isometric compressive stresses to spheroid models, even in the absence of an inducing agent. These results indicate that mechanical stimulation is a potent activator of cellular fusion, suggesting novel avenues to improve experimental reproductive modelling, placental tissue engineering, and understanding disorders of pregnancy development. [ABSTRACT FROM AUTHOR]

Four binary mixtures are isolated from the hydrogen bond liquid crystalline mesogenic complexes comprising methyl malonic acid (MM) and p-n heptyloxy benzoic acid (7BAO) abbreviated as MM + 7BAO & p-n dodecyloxy (12BAO) benzoic acid abbreviated as MM + 12BAO as the precursors exhibit rich phase polymorphism. Variation in the even set of molar proportion in steps of 0.2 leads to the formation of four binary mixtures. Each of the precursors used for the preparation of binary mixtures are treated as X and Y component whose molar ratio varies accordingly. Spectroscopic analysis of the binaries confirms the hydrogen bond existence between the binary mixtures. Phase variance and their textural observations are made through polarizing optical microscopic studies. Thermal properties possessed by these binary mixtures such as phase transition temperatures, enthalpy values, thermal equilibrium, thermal stability, order of phase transition and specific heat capacity are extensively investigated from the differential scanning thermograms obtained for the binaries prepared. These calorimetric investigations suggest the mesophases of the binaries for apt display, thermal and optical applications. [ABSTRACT FROM AUTHOR]

Coffee residues (CRs) were gasified using a laboratory-scale fluidized bed gasifier with an air/steam mixture as the carrier gas. The gasification was conducted at an equivalence ratio (ER) of 0.3, and different operation temperatures (700, 800, and 900 °C) and steam-to-biomass (S/B) ratios (0, 0.75, and 1.5) were applied. Increasing temperature without steam boosted H2 and CO concentrations in producer gas, raising lower heating value (LHV) and cold gas efficiency (CGE) through endothermic reactions like Boudouard, tar cracking, and water-gas formation. At 900 °C, gas had LHV of 3.76 MJ/Nm3 and CGE of 22.47%. It was elevating temperature from 700 to 900 °C and S/B ratio to 1.5 raised H2 and CO concentrations from 2.04 to 8.60% and from 9.56 to 11.8%, respectively. This also increased LHV from 2.23 to 3.89 MJ/Nm3 and CGE from 11.28 to 25.08%. The steam gasification reaction was found to increase the H2 concentration and was thus considered effective in converting CRs to syngas and increasing energy production. Overall, the study successfully demonstrated the feasibility of steam gasification as a means of converting coffee residues to syngas and increasing energy production. The results also highlighted the importance of operating temperature and S/B ratio in improving the gasification process. [ABSTRACT FROM AUTHOR]

Application of natural fibers always faces incompatibility problems due to hydrophilic nature of the fibers which resulted in poor interactions with thermoplastic polymer matrix. The objective of this research was to provide thermoplastic properties to the natural fibers by solvent free esterification using trifluoroacetic anhydride (TFAA) as impelling agent and fatty acids as acylating agent, without any plasticizers or thermoplastic polymer. We also want to make these fibers hydrophobic, potentially addressing incompatibility issues at the fiber matrix interface. The reactivity of esterification varied depending on the chemical composition of the fibers indicated by different values of weight percent gain (WPG). Cotton and flax fiber were chosen for further characterization due to their high content of cellulose which resulted in high ester content. Significant changes in chemical structure were detected using FTIR indicated by substitution of hydroxyl groups with alkyl side chains from fatty acids. This chemical change followed by thermal properties improvement observed by TGA. Thermoplasticity of fatty acids esterified fibers successfully achieved indicated by softening temperature at about 60 and 130°C observed by DSC and TMA, and disappearance of fibers aspect to homogenous and smooth surface of the film. Significant improvements of surface hydrophobicity were also observed by measuring contact angle of water drops to the surface of the film. [ABSTRACT FROM AUTHOR]

A simple, low-cost, and environmentally benign process for synthesizing nanostructured NiO/NiAl2O4 on multiple kinds of carbon nanostructures (CNS) is presented. This method develops polylactic acid (PLA) based waste plastic materials for the producing CNS. These composites (NiO@NiAl2O4/CNS) were examined as potential electrodes in supercapacitors (SC) as they exhibit good charge/discharge reversibility and provide adequate specific capacitance values with a maximum being 1984 F/g at 0.5 A g-1. It is noteworthy that the cycling stability of this sample at 10 A g-1 maintained 101.7% of its initial capacity even after 5000 GCD cycles. An asymmetric supercapacitor (ASC) was built and analyzed, with NiO@NiAl2O4/CNS serving as the cathode and activated carbon serving as the anode of the device. The concluded device has an energy density of 58 Wh kg-1 with a power density of 986 W kg-1 and a SCs of 216.5 F/g. The results showed that the materials mentioned are a great option to use as electrode materials in applications involving the storage of energy. [ABSTRACT FROM AUTHOR]

Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model's robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model's predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system's sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes. [ABSTRACT FROM AUTHOR]

Over the last 20 years, Wi-Fi technology has advanced to the point where most modern devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely questioned since there is no physical barrier separating a wireless network from a wired network, and the security procedures in place are defenseless against a wide range of threats. This study set out to assess federated learning, a new technique, as a possible remedy for privacy issues and the high expense of data collecting in network attack detection. To detect and identify cyber threats, especially in Wi-Fi networks, the research presents FEDDBN-IDS, a revolutionary intrusion detection system (IDS) that makes use of deep belief networks (DBNs) inside a federated deep learning (FDL) framework. Every device has a pre-trained DBN with stacking restricted Boltzmann machines (RBM) to learn low-dimensional characteristics from unlabelled local and private data. Later, these models are combined by a central server using federated learning (FL) to create a global model. The whole model is then enhanced by the central server with fully linked SoftMax layers to form a supervised neural network, which is then trained using publicly accessible labeled AWID datasets. Our federated technique produces a high degree of classification accuracy, ranging from 88% to 98%, according to the results of our studies. [ABSTRACT FROM AUTHOR]

C-axis textured granular thin films of PdCoO2 were deposited at a repletion rate of 3 Hz by pulsed laser deposition technique. A narrow temperature window of 400–500 °C and a high in-situ oxygen pressure of 2 mbar are required to stabilize the rhombohedral [S.G. R 3 ¯ m (166)] phase of PdCoO2. X-ray photoemission spectrum confirms Pd is +1 oxidation state whereas the majority of Co ions are in +3 with a minor fraction in the +4 oxidation state. Raman spectrum shows peaks at 704 cm−1 and 512 cm−1 corresponding to A1g and Eg modes of Pd–O allowed for this system along with an additional low intense peak at 690 cm−1 arising from a weaker Pd–O which shares its oxygen with Co4+in Pd1+–O–Co4+. Magneto-transport studies using six probe geometry show that the films are metallic with a resistivity of 24.09 µΩ cm and a carrier concentration of 2.66 × 1022 cm−3 at room temperature with a high mobility of 9.74 cm2/Vs. [ABSTRACT FROM AUTHOR]

A deep learning (DL) architecture is proposed in this study for the multi-class classification of COVID-19, lung opacity, lung cancer, tuberculosis (TB), and pneumonia. There are two distinct models, namely Classification_1 and Classification_2 in this research. Classification_1 detects the lung diseases and Classification_2 classifies the different lung diseases. The hyperparameters and architecture of the DL models are tuned by grid search optimization (GSO) to get accurate results. To meet the DL criteria, the enormous number of CXR images of COVID-19, lung opacity, pneumonia, lung cancer, TB, and normal images of 3615, 6012, 5856, 20,000, 1400, and 100,192, respectively, were reduced, normalized, and randomly divided. According to the experimental findings, our proposed model beat previous research with 99.82% accuracy in Classifcation_1 and 98.75% accuracy in Classification_2. The suggested paradigm offered greater performance, enabling medical professionals to identify and treat patients more rapidly and effectively. [ABSTRACT FROM AUTHOR]

Real-time vehicle detection is the most challenging and crucial task in intelligent transportation systems. Speed and accuracy are the most anticipated qualities for a vehicle detection model. The existing real-time vehicle detection models lack either one of these qualities, i.e., higher accuracy is achieved at the expense of speed and vice versa. This makes them unfit for real-time deployment, where both speed and accuracy are equally important. Also, occlusion is an inevitable factor that makes detection more complex and affects the system's accuracy. Furthermore, there is no dedicated model for vehicle detection. This study proposes a better one-stage vehicle detection network, DETR-SPP, based on bipartite matching and a transformer encoder-decoder architecture. The feature extraction network, the Convolutional Neural Network (CNN), of the DEtection TRansformer (DETR) object detection model is modified to increase the real-time detection speed and accuracy. The spatial pyramid pooling concept is added to remove the fixed-size constraint and increase the learning capacity of the network. The network is trained only with vehicle classes from the MS COCO 2017 dataset, such as bus, car, motorcycle, and truck. When compared with the other state-of-the-art models, DETR-SPP gives higher accuracy in real-time vehicle detection. On the MS COCO 2017 dataset, the proposed model achieves a better mAP of 51.31%, which is 5.19% higher as compared to the DETR baseline model. Moreover, the proposed DETR-SPP attained a p value of 0.03 while performing the Wilcoxon signed-rank test. Thus, the proposed DETR-SPP is a better model for vehicle detection. [ABSTRACT FROM AUTHOR]

Due to the mental stress of the human, the negative effects are known to be recent decades. Early detections of high level stresses are necessary to stop harmful consequences. Studies have proposed on wearable technologies which detect human stress. This study proposes stress detection systems which use physiological signals of people collected by wearable technologies and attached to human bodies. They can carry it during their daily routine. This work's proposed system includes removal of artifacts in bio signals and feature extractions from these cleaned signals. Since, DL (deep learning) based models are proven to be the best for these analyses, this article uses a random differential GWO (Grey wolf optimization) algorithm for feature extraction and a ML (machine learning) algorithm called RF (random forest) has been used for classification of the human body parameters like activities of the heart, conductance in skins and corresponding accelerometer signals. The proposed stress detection system is implemented with the real time data gathered from 21 participants. This approach can detect the stress of a human and prevent it from early stages with necessary lectures to avoid the negative effects. [ABSTRACT FROM AUTHOR]

The present study aimed to examine elemental contamination near the residential areas of four coal thermal power plants in India. Water and surface soil samples were collected from various geographic sites around Chennai, Korba, Rupnagar, and Udupi power plants. The study identified aluminum (Al), arsenic (As), chromium (Cr), cobalt (Co), copper (Cu), cadmium (Cd), iron (Fe), lithium (Li), magnesium (Mg), manganese (Mn), mercury (Hg), nickel (Ni), zinc (Zn), and lead (Pb) in water and soil samples. Further, soil morphology was also studied using the SEM and EDX. Additionally, using non-cancerous and cancerous risk indices, human health hazards through consuming contaminated water and soil were estimated. Results indicated that Fe and Mn concentrations in water exceeded the standard limits, i.e., the Fe (0.36 mg/l) at site 2 (Chennai), 0.490 mg/l at site 1, and 1.907 mg/l at site 3 (Udupi), while Mn was found as 0.28 mg/l at site 4 (Chennai), 0.27 mg/l at site 3 (Udupi), and 1.52 mg/l at site 4 (Rupnagar). In the case of soil, the maximum metal concentration (mg/kg) was found in Rupnagar, followed by Chennai, Korba, and Udupi. The hazard index (HI) for non-cancerous and cancerous risks was found within the USEPA standard limits, indicating insignificant health risks to the adjoining population of coal thermal power plants. This study provides baseline information regarding the elemental composition in water and soil and the associated health risk status around Indian coal thermal power plants. [ABSTRACT FROM AUTHOR]

A high-definition picture needs more storage space and occupies the memory. For a model to run efficiently, we need to provide a good-quality image, but as we know, we should load a lot of data to obtain accurate results. While it is often applied for compressing or reducing the dimensionality of high-resolution images, it is not specifically designed for pixel reduction. However, we can use principal component analysis (PCA) to achieve pixel reduction by treating the image as a matrix and applying PCA on its pixel values. It is important to note that while PCA can reduce the dimensionality of an image, it does not necessarily reduce the storage size unless the image has a high number of pixels compared to the number of components retained. Experimental results indicate that principal component analysis (PCA) proves effective in addressing these challenges by efficiently reducing the dimensionality of image data while retaining the principal properties of the original image. This reduction in dimensionality not only mitigates data transmission issues but also results in more efficient storage utilization. Additionally, reducing the number of pixels through PCA may result in some loss of detail and image quality. For this reason, we choose PCA, an efficient algorithm for reducing high-dimensional data. In machine learning, a picture with so many pixels will be considered high-dimensional data. The principal component analysis is a decomposition algorithm. It is a fundamental decomposition algorithm which reduces the dimensions in a dataset. Discovering the new variables, referred to as principal components, serves to streamline the solution to the eigenvalue/eigenvectors problem. PCA can be characterized as an adaptive data analysis technology since these variables are crafted to adjust to diverse data types and structures. Comparative analysis proves that the proposed method is more efficient. The study converts the image's pixel dimension from the original data, attaining a pixel rate of 3,155,200-to-100-pixel rate. Therefore, converting a 9,465,600-byte image to a 300-byte image, pixel is usually 3 bytes. Further research will be investigating an alternative dimension reduction approach for solving nonlinear problem space with correlated variables. [ABSTRACT FROM AUTHOR]

Electromagnetic interference (EMI) is a significant source of electromagnetic pollution that disrupts the operation of electronic devices and impacts the functioning of communication and information systems. This has necessitated the development of EMI shielding materials that are recyclable, lightweight, and cost-effective. Moreover, materials with high dielectric properties are in demand for energy storage applications. The present research work aims to develop an electrically conductive woven ramie fiber material through an electroless nickel–phosphorus (NiP)/graphene (Gr) coating to estimate the breaking load resistance, dielectric properties, and EMI shielding effectiveness (SE). From the obtained results, it is inferred that the electrical conductivity of the treated NiP/graphene-coated ramie fiber (T/NiP/Gr/RF) is increased by 74% (14.85 Ω cm−1) when compared to the T/NiP/RF fiber (8.54 Ω cm−1). An impedance analyzer is used to record the dielectric properties at controlled temperatures between 35°C and 60°C in the 50 Hz to 1 MHz frequency range, and the maximum dielectric constant for the NiP/Gr fiber (72.93 and 35°C) and (29.75 and 60°C). respectively, is observed at 50 Hz. The EMI SE is evaluated using a Keysight 9374A-vector network analyzer test setup at 8–12 GHz (X-band) frequency. The addition of graphene nanoparticles with the treated NiP-coated fiber increases the EMI SET from 47.12 dB to 51 dB. The improved dielectric properties and EMI SET of the T/NiP/Gr/RF material indicate its suitability for use in energy storage and EMI shielding applications. [ABSTRACT FROM AUTHOR]

Water pollution has become a major environmental threat owing to the expeditious development of industries. These industries rapidly release wastewater containing harmful dyes (organic pollutants), which significantly affect the lives of humans and aquatic organisms to a huge extent. Therefore, the elimination of the pollutants in the contaminated water has become the primary need of the moment that is to be treated before it discharges into the environment. This article mainly focuses on the adsorption mechanism of the organic dye methylene blue from wastewater under sunlight using a zinc-metal organic framework (Zn-MOF). The Zn-MOF, being a three-dimensional porous structure was synthesized by the direct precipitation method using zinc metal ions and terephthalic acid (or) benzene-1,4-dicarboxylic acid as a connecting linker. Then, morphological and optical characterizations, along with the analysis of thermal and surface parameters, were performed for the synthesized Zn-MOF. The synthesized Zn-MOF appeared to be in a cubic form with a pore volume of about 0.006 cc/g. It was also found to have a surface area of 4.705 m2/g, with an absorbance range of 239.39 nm. The elemental composition of zinc in the synthesized Zn-MOF was approximately 25.41%, proving that the synthesized MOF is a Zinc-based MOF. As the results showed many properties related to the adsorption mechanism, Zn-MOF was chosen to proceed with the adsorptive application of removing the methylene blue dye from wastewater under sunlight. This application provides an efficient solution for the treatment of wastewater through the adsorption property of Zn-MOF, thereby proving its potential as an adsorbent. [ABSTRACT FROM AUTHOR]

This research paper presents the development and implementation of a real-time communication system that utilizes LEDs for transmitting text, image, and audio through Free Space Optical Communication (FSOC) and Underwater Wireless Optical Communication (UWOC) channels. The impact of atmospheric and oceanic attenuation and turbulences are considered for the performance analysis of the proposed system. The various atmospheric weather conditions like light fog, dense fog, and heavy rain are considered for FSOC while in the case of UWOC, effects like pure water, and seawater with turbulence and without turbulence are considered. The outcomes of the experiments and simulations, including quality factors, eye diagrams, and bit error rates, are plotted and discussed in the results. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a phenomenon that connects all physical objects to one another and makes it possible for them to interact and collaborate via the web and other technologies. This phenomenon is also known as the internet of things. It is necessary for the devices to be self-sufficient, easily locateable, and unobtrusive to the human eye. A streamlined mathematical model that contains a variety of distinct laws serves as a metaphor for our whole body of information. This photovoltaic model represents our knowledge in its totality. This all-encompassing comprehension is built on top of a compilation of statutes, which serves as the framework. Depending on the goal of the user, it is possible that the rules will need to be altered in order to either maximize the benefits or minimize the amount of money spent on electricity. This will depend on how the rules are changed. [ABSTRACT FROM AUTHOR]

This article explores the challenges and barriers in accelerating drug product development and approval, with a focus on new modalities like peptides, antibody drug conjugates, and nucleotide-based therapies. It emphasizes the complexity and attrition that can arise from the unique requirements of each modality in preclinical and drug product development. The article also discusses the need for simplification and acceleration strategies in modeling, data/artificial intelligence, and automation in preclinical development. Regulatory considerations and the global landscape in early medicines development are also addressed. The article concludes by suggesting strategies for addressing the identified barriers, including better understanding of the mechanism of action, building pre-competitive knowledge bases, and fostering collaboration between academia, industry, and regulatory agencies.In addition, this document highlights the need for an analytical toolkit to speed up the development of new drugs and improve preclinical toxicology studies. The panel emphasizes the importance of using both traditional and novel techniques, such as nuclear magnetic resonance and organ-on-a-chip systems, to assess the safety of new drugs. The use of machine learning and in silico approaches is also highlighted as a way to analyze preclinical data and reduce the reliance on animal testing. The document further addresses the regulatory barriers faced in developing novel drugs and the initiatives implemented by regulatory bodies to support their accelerated development. The panel suggests a collaborative approach between health authorities and industry to overcome these challenges and bring innovative medicines to patients in a timely manner. [Extracted from the article]

In this work, the influences of Deep Cryogenic Treatment (DCT) at − 196 °C for 24 h on heat treated Electroless Nickel- Phosphorous (NiP)-coated substrates are studied. Adhesion of coating, structure and composition of coating and corrosion properties of the coating have been evaluated. AISI 1020 steel samples are coated with NiP by means of an autocatalytic reaction along with mechanical stirring. The coated samples are then heated to 400, 600 and 800 °C to form an interdiffusion layer. Then, DCT is performed at −196 °C with a soaking time of 24 h. Micro hardness, adhesion test, x-ray diffraction (XRD), scanning electron microscopy (SEM), and electrochemical corrosion tests are used to evaluate the coating surface. Scanning electron microscope analysis and XRD of the DCT NiP substrate ensured the formation of ß-phase precipitates. The examination of coating adherence is judged to be sufficient and acceptable for DCT NiP as per VDI 3198 standard. A minor variation in micro hardness is observed after DCT with a gradual increase in 2%. This ensures that, DCT does not affect the mechanical properties of heat treated NiP samples. However, accelerated corrosion test Ecorr is displaced in the direction of the active field for DCT NiP samples from − 0.171 to −0.020 mV this increase in corrosion resistance may be due to phase transformation. In addition to this, reduced icorr showed increase in corrosion resistance of the substrate. Similarly the corrosion resistance identified through Electrochemical Impedance Spectroscopy (EIS) showed an overall increase in 9% in corrosion resistance after DCT and compared with corrosion morphology. It creates a new avenue in the field of materials in aerospace and satellite applications. [ABSTRACT FROM AUTHOR]

This research investigates the hot corrosion behavior of Inconel 625 similar joints welded using pulsed current gas tungsten arc welding (PCGTAW). Specific emphasis has been given to the microstructural changes at the fusion zone and weld interface while employing two different Ni-Mo-rich fillers (ERNiCrCoMo-1 and ERNiCrMo-4). The microstructure of the weldments was evaluated using scanning electron microscopy (SEM) and optical microscopy (OM) techniques. The weld joints employed with ERNiCrMo-4 filler showed slightly higher strength than that of ERNiCrCoMo-1, and the fractures were found on the base metal regions. The performance of the alloy 625 bi-metallic joints has been ascertained by exposing both cyclic molten salt K2SO4 + 60% NaCl and an air oxidation environment at 700 °C. A thermogravimetric plot was employed to determine the weldment kinetics of corrosion. SEM/EDS systematically examined the oxide scales formed on the weldments, and x-ray diffraction (XRD) was used to identify the phases of the corrosion products. The test results revealed that the corroded samples in the salt condition experienced severe corrosion attacks as compared to air conditions by sulfidation and chlorination. Based on the specific outcomes of this study, ERNiCrMo-4 filler is recommended for joining these similar combinations of welds and it imparts better corrosion resistance at elevated temperatures in simulated boiler environments. [ABSTRACT FROM AUTHOR]

Detecting facial emotions among people is a crucial task in social communication, as it reflects their internal character. In the future, virtual face emotion detection will play a vital role in various fields, such as virtual human detection, security systems, online games, human psychology analysis, virtual classrooms, and monitoring abnormalities in patients. Integrating facial emotion detection into virtual human detection enhances the entire virtual experience, infusing interactions with authenticity, emotional intelligence, and customization for individual users. Human emotions, depicted on the face represent the brain's reactions that can be captured in the form of video or image for accurate diagnosis. This paper introduces a technology-aided face emotion detection system using convolutional neural networks (CNN). The CNN model performs the emotion detection function by executing image pre-processing, feature extraction, and image classification. Computational modules within the neural network extract features from images to enhance prediction. The proposed CNN model uses data augmentation, max pooling, and batch normalization techniques to expand facial emotion classification and improve performance and generalization. Additionally, ResNet50 architecture used with CNN improves accuracy and reduces error rate with identity mapping. Comparing performance metrics, accuracy, loss and complexity to existing models, the proposed models outperform them. The proposed CNN achieves a maximum of 15.53% higher accuracy and 25.22% lower loss in face emotion detection than the lowest-performing existing model. [ABSTRACT FROM AUTHOR]

During a 3 year examination of object movement tracking in Hosur forest border areas found, different elephant detection methods were used to track the movement. In this paper, problems such as Human–Elephant Conflicts (HEC), poaching and crop raiding were evaluated due to elephant movement. By mapping the elephant movement, it is found that elephant movements were seasonal and used similar paths for movements. In the proposed approach, elephant movement from the forest areas to the nearby villages was mapped and possible mitigation measures to overcome the human elephant conflicts were discussed. A survey observation work is made in Hosur forest areas during 1st April 2020 to 30th June 2020 to understand the elephant movement and spatial distribution. Real time data collection made from the villages around the forest areas, by making field visits. Spatial autocorrelation techniques are used in analyzing the spatial distribution and movement pattern of elephants. ArcGIS software and Getis-Ord Gi* were used to determine the spatial distribution of elephant movement. As a result, it is found that elephant movements were seasonal based such as rainfall decline in other part of forest areas and elephants tends to migrate occasionally. In order to overcome the problems of elephant movement into the human living areas, mitigation measures described has to be implemented. [ABSTRACT FROM AUTHOR]

The plant–parasitic Root Knot Nematodes (Meloidogyne spp.,) play a pivotal role to devastate vegetable crops across the globe. Considering the significance of plant–microbe interaction in the suppression of Root Knot Nematode, we investigated the diversity of microbiome associated with bioagents-treated and nematode-infected rhizosphere soil samples through metagenomics approach. The wide variety of organisms spread across different ecosystems showed the highest average abundance within each taxonomic level. In the rhizosphere, Proteobacteria, Firmicutes, and Actinobacteria were the dominant bacterial taxa, while Ascomycota, Basidiomycota, and Mucoromycota were prevalent among the fungal taxa. Regardless of the specific treatments, bacterial genera like Bacillus, Sphingomonas, and Pseudomonas were consistently found in high abundance. Shannon diversity index vividly ensured that, bacterial communities were maximum in B. velezensis VB7-treated soil (1.4–2.4), followed by Root Knot Nematode-associated soils (1.3–2.2), whereas richness was higher with Trichoderma konigiopsis TK drenched soils (1.3–2.0). The predominant occurrence of fungal genera such as Aspergillus Epicoccum, Choanephora, Alternaria and Thanatephorus habituate rhizosphere soils. Shannon index expressed the abundant richness of fungal species in treated samples (1.04–0.90). Further, refraction and species diversity curve also depicted a significant increase with maximum diversity of fungal species in B. velezensis VB7-treated soil than T. koningiopsis and nematode-infested soil. In field trial, bioagents-treated tomato plant (60% reduction of Meloidogyne incognita infection) had reduced gall index along with enhanced plant growth and increased fruit yield in comparison with the untreated plant. Hence, B. velezensis VB7 and T. koingiopsis can be well explored as an antinemic bioagents against RKN. [ABSTRACT FROM AUTHOR]