Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. The models were developed using limited amount of daily water level and meteorological data from 2016 to 2018. Different input scenarios were tested to investigate the performance of the models. The results of the evaluation showed that the MLP model outperformed both the LSTM and the XGBoost models in predicting water levels, with an overall accuracy score of 0.871 compared to 0.865 for LSTM and 0.831 for XGBoost. No noticeable improvement has been achieved after incorporating meteorological data into the models. Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. In terms of different time horizons, the LSTM model was found to be more accurate than the MLP and XGBoost model when predicting 7 days ahead, demonstrating its superiority in capturing long-term dependencies. Therefore, it can be concluded that each ML model has its own merits and weaknesses, and the performance of different ML models differs on each case because these models depends largely on the quantity and quality of data available for the model training.
Andaliman (Z. acanthopodium DC) is a kind of flowering plant that belongs to the Rutaceae family. The habitats are found in southwestern China (Guangxi, Guizhou, Sichuan, Tibet, and Yunnan), Bangladesh, Bhutan, northern India, and northeastern India (Arunachal Pradesh, Assam, Manipur, Meghalaya, Nagaland, Sikkim, Uttar Pradesh, and West Bengal), Nepal, Laos, Burma, Vietnam, North Sumatra highlands, Peninsular Malaysia, and northern Thailand. Andaliman is indigenous to North Sumatra, more specifically the Toba Samosir District, North Tapanuli, and the Dairi region. The phytochemical investigation showed some terpenoids as well as other compounds such as alkaloids, flavonoids, glycosides, tannins, and saponins, but they have not been entirely identified. In Indonesia, the plant is employed both in the culinary industry as an additive for flavoring food and in the traditional medical system for treating various diseases. It was reported to possess antibacterial, antifungal, anti-inflammatory, anticancer, cardioprotective, hepatoprotective, nephroprotective, and wound healing properties, alongside other activities related to pregnancy that were tested in vitro and in vivo. The results of the investigation were based on previously published studies. This review serves as information and a summary, thereby making further exploration of Andaliman to be easier.
The calcium looping cycle (CaL) possesses outstanding CO2 capture capacity for future carbon-capturing technologies that utilise CaO sorbents to capture the CO2 in a looping cycle. However, sorbent degradation and the presence of inert materials stabilise the sorbent, thereby reducing the CO2 capture capacity. Consequently, the CaO sorbent that has degraded must be replenished, increasing the operational cost for industrial use. CaO sorbents have been modified to enhance their CO2 capture capacity and stability. However, various CaO sorbents, including limestone, dolomite, biogenesis calcium waste and industrial waste, exhibit distinct behaviour in response to these modifications. Thus, this work comprehensively reviews the CO2 capture capacity of sorbent improvement based on various CaO sorbents. Furthermore, this study provides an understanding of the effects of CO2 capture capacity based on the properties of the CaO sorbent. The properties of various CaO sorbents, such as surface area, pore volume, particle size and morphology, are influential in exhibiting high CO2 capture capacity. This review provides insights into the future development of CaL technology, particularly for carbon-capturing technologies that focus on the modifications of CaO sorbents and the properties that affect the CO2 capture capacity.
In this technology era, sustainable construction practices have become quite imperative. The exploration of alternative materials to reduce the environmental footprint is of paramount importance. This research paper delves into an exhaustive investigation concerning the utilization of recycled coarse aggregates (RCA) and rubber particles (RP) in concrete. It contributes to the growing body of knowledge aimed at fostering sustainable development in the construction industry by reducing waste, promoting recycling, and mitigating the environmental footprint of building materials. The objective of the study is to evaluate the potential benefits and limitations associated with incorporating these materials, thereby providing a sustainable alternative to conventional concrete. In this research, construction and demolition waste were recycled and used as RCA as a fractional switch of natural coarse aggregate (NCA) from 0% to 100%, with an increment of 20% replacement of NCA in concrete. The RP received from discarded tires generated as automobile industry waste were used as a volumetric fractional substitution of sand in concrete from 0% to 20%, with a 5% increment. No pre-treatment for RCA and RP was carried out before their utilization in concrete. A total of 26 mixes, including control concrete without NCA and RP, with a design strength of 40 MPa, were prepared and tested. Concrete mixes were examined for workability, density, mechanical, and durability properties. It was found that the concrete with 60% RCA and 10% RP showed satisfactory results in evaluation with the strength parameters of control concrete, as the compressive strength obtained for this concrete mix is 40.18 MPa, similar to the control mix. The optimization for RCA and RP was conducted using Response Surface Methodology (RSM). The major concern observed was a rise in water absorption with an increase in the percentage replacement of NCA and natural sand by RCA and RP. Findings from the investigation illustrate a promising prospect for the use of RCA and RP in concrete applications, displaying competent mechanical properties and enhanced durability under certain conditions, offering a viable option for environmentally friendly construction practices. However, the research also sheds light on some constraints and challenges, such as the variability in the quality of RCA and the necessity for meticulous quality control to ensure the reliability and consistency of the end product. It is discerned that further refinement in processing techniques and quality assurance measures is pivotal for mainstream adoption of RCA and RP in concrete construction.
In developing countries, farming businesses are dominated by small-scale farmers with limited resources. Such farmers are subjected to high risks, influencing the success rate of their agricultural endeavors. This study, conducted in Aceh Province, Indonesia, measured the risk tolerance among six groups of farmers with businesses based on the following seasonal commodities: paddy, corn, soy, chili, potato, and tomato. A total of 360 respondents were surveyed and 54 key respondents interviewed. A Likert scale was used to assess the risk tolerance levels of the farmers, and ordinal regression analysis to analyze the factors influencing risk tolerance. Paddy, chili, and potato farmers had a relatively high tolerance to farming risks, whereas corn and tomato farmers showed a moderate tolerance. Soy farmers were classified into the low risk tolerance category. Ordinal analysis indicated that the risk tolerance of farmers in each commodity group was influenced by specific factors. Overall, it was found that the farmers' attitudes to risk tolerance were significantly affected by the following factors: experience, education, farming income, capital, land status, and land size. An intervention strategy including improvements in the curriculum, actors, network, scope of clusters, and technology are among the strategies required to positively improve farmers' perceptions and increase their tolerance to farming risks.
Micro-expressions (ME) are rapidly occurring expressions that reveal the true emotions that a human being is trying to hide, cover, or suppress. These expressions, which reveal a person's actual feelings, have a broad spectrum of applications in public safety and clinical diagnosis. This study provides a comprehensive review of the area of ME recognition. A bibliometric and network analysis techniques is used to compile all the available literature related to ME recognition. A total of 735 publications from the Web of Science (WOS) and Scopus databases were evaluated from December 2012 to December 2022 using all relevant keywords. The first round of data screening produced some basic information, which was further extracted for citation, coupling, co-authorship, co-occurrence, bibliographic, and co-citation analysis. Additionally, a thematic and descriptive analysis was executed to investigate the content of prior research findings, and research techniques used in the literature. The year wise publications indicated that the published literature between 2012 and 2017 was relatively low but however by 2021, a nearly 24-fold increment made it to 154 publications. The three topmost productive journals and conferences included IEEE Transactions on Affective Computing (n = 20 publications) followed by Neurocomputing (n = 17) and Multimedia tools and applications (n = 15). Zhao G was the most proficient author with 48 publications and the top influential country was China (620 publications). Publications by citations showed that each of the authors acquired citations ranging from 100 to 1225. While publications by organizations indicated that the University of Oulu had the most published papers (n = 51). Deep learning, facial expression recognition, and emotion recognition were among the most frequently used terms. It has been discovered that ME research was primarily classified in the discipline of engineering, with more contribution from China and Malaysia comparatively.
A 3 × 2 factorial experiment was conducted to investigate the effects of dietary lysine on growth performance, body indices, feed intake, feed efficiency, whole body nutrient composition and amino acid deposition in two successive generations (16th and 17th) of GIFT (Oreochromis niloticus). Three diets containing different levels of lysine at 1.16%, 1.56% and 2.41% were prepared for the feeding trial. Triplicate groups of fish with an initial body weight of 155 g were fed to apparent satiation for 10 weeks in a recirculating aquaculture system. Apparent digestibility coefficients (ADC) of dry matter, crude protein, crude lipids, and total carbohydrates were measured in the experimental diets. At the end of the experiment, no interactions between dietary lysine levels in diet and fish generation were observed on all parameters except for the condition factor (CF) and ADC of crude protein. However, dietary lysine level significantly affected the final weight, weight gain, thermal unit growth coefficient (TGC), protein efficiency ratio (PER) and ADC of dry matter regardless of the fish generation. Final weight, weight gain and TGC of fish were the highest in fish fed 2.41% dietary lysine in diet or 6.52% lysine in the protein. PER was the lowest in fish fed 1.16% dietary lysine. The final weight and the body's accumulation of isoleucine, phenylalanine, and alanine were significantly affected by the fish generation, with the 17th generation having the best performance. Increase growth and higher lysine requirement observed in the improved generation (17th) compared to the (16th) generation at grow out phase indicating that genetic improvement may have changed the dietary lysine requirement.
Animal tuberculosis (TB) is a contagious and chronic disease caused by mycobacteria belonging to theMycobacterium tuberculosis complex (MTBC) in domestic and wild animals. MTBC strains infection has been confirmed in many animal species in Nigeria, including captive wildlife, cattle, dromedary camels, goats, and pigs. Despite widespread infection and the potential impact of the disease on public health, active surveillance and control strategies are absent in Nigeria. This study aimed to conduct the first comprehensive meta-analysis to assess the distribution of tuberculosis and analyze the potential moderators of infection in animals in Nigeria. Eligible studies (sixty-one (Cadmus et al., 2014) [61] prevalence and seven (Menzies and Neill, 2000) [7] case reports) were retrieved and included in the analysis. The analyses showed an overall pooled TB prevalence of 7.0% (95% CI: 6.0-8.0) comprising of infection distributed in cattle (8.0%, 95% CI: 7.0-8.0), goats (0.47%, 95% CI: 0-1.2), sheep (0.27%, 95% CI: 0.14-0.46), camels (13.0%, 95% CI: 0-47), and wildlife (13.0%, 95% CI: 9-16) respectively. The occurrence of infection was significantly moderated by the publication periods, geographical location, sample size, and detection methods. TB prevalence was heterogeneous across several predictors, with the year of publication exhibiting a higher rate (46%) of the detected heterogeneity. These findings should provide policy-relevant information to guide the design and establishment of prevention and control measures amenable to the local situations in Nigeria.
This quantitative study investigates the relationships of green human resource management (GHRM) practices (e.g., green training and involvement, green recruitment, green performance management and compensation, and green transformational leadership) on green organizational culture and pro-environmental behavior, and the moderating role of green social capital and green values. This study adopts a cross-sectional design and collects quantitative data from 232 respondents working in top-to middle-level managerial positions in medium and large enterprises using a questionnaire survey after obtaining a list of companies from the Securities and Exchange Commission of Lahore, Rawalpindi, and Multan in Pakistan, applying the snowball sampling technique. A combined approach including partial least squares structural equation modeling and necessary condition analysis is employed to unravel the underlying mechanism between GHRM practices, green organizational culture, and pro-environmental behavior using Smart PLS version 4. The findings reveal that green transformational leadership (β = 0.267, p
In this article, the prediction of fatality accident rate at methanol (MeOH) plant was studied using different assessment methods. The prediction method was performed and simulated using HYSYS, ALOHA, MARPLOT, and MATLAB software. Recent studies for pressure variation up to 442 bar in MeOH synthesis by carbon dioxide (CO2) hydrogenation showed that three times more MeOH was produced than in conventional plants, with 90% CO2 conversion and 95% MeOH selectivity. However, safety concerns were noted when MeOH production was operated at pressures above 76-500 bar. Therefore, a safety assessment of the pressures between 76 and 500 bar was performed to predict the fatality rate at the MeOH plant. Adaptive Neuro-Fuzzy Inference System (ANFIS) was compared with a conventional analysis by using the consequence method to predict the fatality rate. First, 26 input parameters were simulated in HYSYS, ALOHA, and MARPLOT software by using the consequence method. Then, the input parameters were reduced to six, namely, pressure, mass, volume, leakage size, wind speed, and wind direction, for prediction using ANFIS tool in MATLAB. This study aimed to highlight the accuracy of the fatality rate prediction by using the ANFIS method. In this manner, accurate prediction of fatality rate for MeOH plant incidents was achieved. The prediction values for the ANFIS method was validated using the standard error of the regression. The percent error measurement obtained the lowest regression of 0.0088 and the lowest percent error of 0.02% for Hydrogen (H2) Vapor Cloud Explosion (VCE) ident. Therefore, the ANFIS method was found to be a simpler and alternative prediction method for the fatality rate than the conventional consequence method.
Dimensionality reduction plays a pivotal role in preparing high-dimensional data for classification and discrimination tasks by eliminating redundant features and enhancing the efficiency of classifiers. The effectiveness of a dimensionality reduction algorithm hinges on its numerical stability. When data projections are numerically stable, they lead to enhanced class separability in the lower-dimensional embedding, consequently yielding higher classification accuracy. This paper investigates the numerical attributes of dimensionality reduction and discriminant subspace learning, with a specific focus on Locality-Preserving Partial Least Squares Discriminant Analysis (LPPLS-DA). High-dimensional data frequently introduce singularity in the scatter matrices, posing a significant challenge. To tackle this issue, the paper explores two robust implementations of LPPLS-DA. These approaches not only optimize data projections but also capture more discriminative features, resulting in a marked improvement in classification accuracy. Empirical evidence supports these findings through numerical experiments conducted on synthetic and spectral datasets. The results demonstrate the superior performance of the proposed methods when compared to several state-of-the-art dimensionality reduction techniques in terms of both classification accuracy and dimension reduction.
Recent advancements in CdTe solar cell technology have introduced the integration of flexible substrates, providing lightweight and adaptable energy solutions for various applications. Some of the notable applications of flexible solar photovoltaic technology include building integrated photovoltaic systems (BIPV), transportation, aerospace, satellites, etc. However, despite this advancement, certain issues regarding metal and p-CdTe remained unresolved. Besides, the fabrication of a full-working device on flexible glass is challenging and requires special consideration due to the unstable morphology and structural properties of deposited film on ultra-thin glass substrates. The existing gap in knowledge about the vast potential of flexible CdTe solar cells on UTG substrates and their possible applications blocks their full capacity utilization. Hence, this comprehensive review paper exclusively concentrates on the obstacles associated with the implementation of CdTe solar cells on UTG substrates with a potential back surface field (BSF) layer. The significance of this study lies in its meticulous identification and analysis of the substantial challenges associated with integrating flexible CdTe onto UTG substrates and leveraging Cu-doped ZnTe as a potential BSF layer to enhance the performance of flexible CdTe solar cells.
The polychaete Diopatra claparedii Grube, 1878 is among those organisms successfully carrying out full body regeneration, including the whole nervous system. Thus, D. claparedii potentially can be regarded for the nervous system regeneration (NSR) study. However, data on the property of its nervous system and the NSR profile are still lacking. In this study, we investigated the morphology of D. claparedii anterior nervous system (ANS) and examined the cellular and molecular profiles on its early anterior NSR. The nervous system of D. claparedii consists of a symmetry brain with nerves branching off, circumpharyngeal connectives that connect the brain and nerve cord as well as obvious segmental ganglia. Moreover, we identified changes in the cellular condition of the ganglionic cells in the regenerating tissue, such as the accumulation of lysosomes and lipofuscins, elongated mitochondria and multiple nucleoli. Furthermore, mRNA of tissues at two regenerating stages, as well as intact tissue (non-regenerating), were sequenced with Illumina sequencer. We identified from these tissues 37,248 sequences, 18 differential expressed proteins of which upregulated were involved in NSR with noelin-like isoform X2 turned up to be the highest being expressed. Our results highlight the cellular and molecular changes during early phase of NSR, thus providing essential insights on regeneration within Annelida and understanding the neurodegenerative diseases.
The pilocarpine animal model of status epilepticus is a well-established, clinically translatable model that satisfies all of the criteria essential for an animal model of status epilepticus: a latency period followed by spontaneous recurrent seizures, replication of behavioural, electrographic, metabolic, and neuropathological changes, as well as, pharmacoresistance to anti-epileptic drugs similar to that observed in human status epilepticus. However, this model is also characterized by high mortality rates and studies in recent years have also seen difficulties in seizure induction due to pilocarpine resistant animals. This can be attributed to differences in rodent strains, species, gender, and the presence of the multi-transporter, P-glycoprotein at the blood brain barrier. The current paper highlights the various alterations made to the original pilocarpine model over the years to combat both the high mortality and low induction rates. These range from the initial lithium-pilocarpine model to the more recent Reduced Intensity Status Epilepticus (RISE) model, which finally brought the mortality rates down to 1%. These modifications are essential to improve animal welfare and future experimental outcomes.
Organic loading influences the effectiveness of producing biogas through anaerobic digestion. This study set out to investigate the effect of organic loading on the anaerobic mesophilic digestion of cow dung, the parameters involved in the digestion process and to evaluate the kinetics. Anaerobic digestion of cow dung at different organic loading (gVS/L) of 14 gVS/L, 18gVS/L, 22 gVS/L, 26 gVS/L and 30 gVS/L were investigated. Increasing the organic loading increased the methane yield of the cow dung. The highest cumulative methane yield was observed at 30 gVS/L with 63.42 mL CH4/gVS while the highest biogas yield was reported at 192.53 mL/gVS with the highest methane content of 89%. In addition, the modified Gompertz model equation with an R2 of 0.9980 demonstrated strong consistency and a good fit between predicted and experimental data. The high number of substrates added to the systems when increasing the organic loading increased the λ and slow down the nutrient transport and hydrolysis. This study provides current information on the effects of organic loading on the anaerobic digestion of cow dung in batch mode, including experimental conditions and operational parameters.
Climate change (CC) is one of the greatest threats to human health, safety, and the environment. Given its current and future impacts, numerous studies have employed computational tools (e.g., machine learning, ML) to understand, mitigate, and adapt to CC. Therefore, this paper seeks to comprehensively analyze the research/publications landscape on the MLCC research based on published documents from Scopus. The high productivity and research impact of MLCC has produced highly cited works categorized as science, technology, and engineering to the arts, humanities, and social sciences. The most prolific author is Shamsuddin Shahid (based at Universiti Teknologi Malaysia), whereas the Chinese Academy of Sciences is the most productive affiliation on MLCC research. The most influential countries are the United States and China, which is attributed to the funding activities of the National Science Foundation and the National Natural Science Foundation of China (NSFC), respectively. Collaboration through co-authorship in high-impact journals such as Remote Sensing was also identified as an important factor in the high rate of productivity among the most active stakeholders researching MLCC topics worldwide. Keyword co-occurrence analysis identified four major research hotspots/themes on MLCC research that describe the ML techniques, potential risky sectors, remote sensing, and sustainable development dynamics of CC. In conclusion, the paper finds that MLCC research has a significant socio-economic, environmental, and research impact, which points to increased discoveries, publications, and citations in the near future.
The current literature suggests that a lack of integration between engineering for performance shaping factors (PSFs) and workplace energy management (WEM) is a significant barrier to improving energy management practices (EMP) and power plant efficiency. The study identified three research objectives in response to this research gap: (1) conduct a systematic literature review to analyze current studies; (2) develop a novel integrative model capable of predicting EMP; and (3) test the novel model's validity and reliability through an empirical study in thermal power plants. In this study, a group of academic and energy experts designed research instruments to achieve the study's objectives, which were then pilot-tested. Partial least square structural equation modeling was utilized to analyze the data in this study. The study successfully developed a new model for future sustainable energy management in power plants and a new model integrating the PSFs and WEM to predict power plant energy performance, aiming to enhance communication between operators and EMP in power plants. The model exhibited exceptional explanatory and predictive abilities, yielding a strong fit. Furthermore, the incorporation of success factors associated with PSFs positively influenced the EMP. The data set followed a normal distribution, confirming the model's reliability and validity. Significantly, this study achieved a breakthrough by being the first to integrate success factors for PSFs and WEM in thermal power plants, thus effectively addressing an unexplored area of research. However, the inconsistencies in the current studies emphasize the necessity for additional investigations into the strategy of PSFs in EMP within power plants.
The present study presents a retrospect into environmental Kuznets curve hypothesis (EKC). The EKC debate is dated over four decade long and worthy of empirical scrutiny. To this end, the present study leverages on over 200 previous studies curated from SCOPUS and Web of science (WOS) core collection database respectively. The present study also presented both literature schematic on the evolution, trends, gaps, and future directions on the EKC debate. This paper endeavors to enhance our comprehension of the inherent paradoxes present in sustainability discourses by delving into the fundamental assumptions underlying the Environmental Kuznets curve (EKC). By conducting a bibliometric analysis, we aim to shed light on the factors contributing to the prominence of thematic keywords within sustainability discourses. This study seeks to provide valuable insights into these dynamics and implications on sustainability debates. Key empirical findings outlines predominant and influential studies and journal outlets on the theme under consideration. The present study bibliometric analysis displays that Ozturk i. with 13 published papers 3153 citations and a link strength of 2, Dogan e. Had 7 papers with 2190 citations with no link strength, Shahbaz. B 7 papers 1347 citations and 1 link strength, Saboori b.7 papers 677 citations 1 strength link and Liu y. 6 papers 582 citations with no link strength. From a policy dimension, the present bibliometric study presents valuable depth on the evolution and development of the EKC phenomenon by identifying's the extant literature leaders, action-step for future studies on environmental sustainability without compromise on economic growth as the EKC theme express the tradeoff between economic growth and environmental degradation. Further insights are rendered in the concluding section.
Biofloc Technology (BFT) is proven to be the fulcrum of sustainable recirculating aquaculture system especially under zero water discharge condition. The efficiency of BFT system is reinforced by an unswerving microbial community in the system. Several researchers have made copious reports on the microorganisms in BFT and identified heterotrophic bacteria predominant in the microbial composition. A summary of these researches considers these microorganisms playing the role of chemo-photosynthetic autotrophs, organic detoxifiers, probiotic, decomposers/bioflocculants, bio-leachers and pathogens. Although these functional roles are well identified, the reports have failed to sufficiently illustrate the borderline at which these microbial communities fail to serve their beneficial roles in BFT system. This review paper firstly presents a snapshot of some indispensable water quality conditions and zootechnical variables aided by the microbial community in floc as well as the amphibolic process that synthesizes nutrient from the organic deposit in BFT. Furthermore, information on the microbial community in BFT is evaluated to have Bacillus sp., Lecane sp. and Pseudomonas sp. serving all-encompassing role in BFT while Vibrio sp. and Enterobacter sp. are pathogenic under unsuitable water quality conditions. Functional characterisation of the commonly reported microorganisms in BFT categorised 21.95 % as most critical, whose abundance indicates an efficient BFT.
This study provides an alternative agenda to better explain the Belt and Road Initiative's (BRI's) technological connotations in Bangladesh using the Game Theory and Demand Curve approaches. BRI can proceed as a means to technology development for Bangladesh based on foreign direct investment (FDI) spillover effects that ranked China as the top FDI source, with 1159.42 million USD invested in 2018-2019. The findings suggest that motivated by mutual interests of economic transformation (China) and technological requirements (Bangladesh), BRI offers a bargaining game of cooperation. Thus, while economic transformation may force China to relocate its garment factories, Bangladesh's low wages and geopolitical location give it a superior position regarding relocation. The technological effects of such relocation will be two-fold: exchanges of tacit knowledge (conventional) and techno-based infrastructural support (component) that align with the proposed technology development framework on a macro level. More conventional technological projects and additional sector-based technology transfer are required to amplify BRI's technological forecasts. Moreover, to encourage more abundant FDI, bank loan interest must be decreased, and political stability has to be ensured. Both survey-based fieldwork and projects-based qualitative research need to be conducted to discover BRI's tangible technological implications.