bluuebunny/crossref_metadata_2025 huggingface dataset:
Data quality assessment of pig movements in the Norwegian Livestock Register reveals challenges for outbreak preparedness
Authors: Dean Katharine Rose , Grøntvedt Carl Andreas , Hopp Petter , Viljugrein Hildegunn , Wolff Cecilia
Published: October 2024

Background
Animal movements are an important pathway for the spread of pig diseases. Traceability systems provide data for the competent authorities to prevent and manage infectious disease outbreaks. In Norway, batch-level pig movements are documented in the Norwegian Livestock Register. The purpose of our study was to evaluate the quality of pig movement data in the Norwegian Livestock Register for 2022. We assessed the quality in terms of accuracy, completeness and timeliness for the purpose of outbreak preparedness. We used secondary governmental and industry registers for external validation.
Results
The Norwegian Livestock Register contained all the variables needed for tracing pig movements between farms and to slaughterhouses. The register had high accuracy for individual records of between farm movements, however, it lacked completeness. By comparing farm-level movements to the Register for Carcass Deliveries, we found that 41% of sending farms had fewer movements than expected reported in the Norwegian Livestock Register. Similarly, a quarter of all finisher farms did not report receiving any pigs for 2022. Using indicator farms, we show that three slaughterhouses did not correctly report live animal movements on behalf of owners. Lastly, we found that 41% of records were registered after the deadline of seven days.
Conclusions
The competent authorities need accurate, complete, and timely data on livestock movements to control rapidly spreading diseases. Based on our assessment, we found that pig movement data in the Norwegian Livestock Register lacked sufficient quality to serve this purpose. Instead, we recommend that movement data are additionally obtained through traditional epidemiological methods during outbreaks, such as from primary records on farms. Reporting from slaughterhouses and farms in sow pools should be targeted for improving the completeness and timeliness of the register data. Finally, the measures presented here should be used to develop real-time monitoring of the data quality.
Influence of Memristive Activated Gradient on Chaotic Dynamics In Discrete Neural Networks
Authors: Yu Fei , He Ting , He Shaoqi , Tan Bohong , Yao Wei , Lin Hairong
Published: October 2024

The hyperbolic tangent function is a commonly used activation function in Hopfield neurons, where its activation gradient serves as a gain scaling parameter reflecting the response speed of neuronal electrical activity. This paper proposes a novel method of integrating memristors with discretely coupled neural networks by using an exponential memristor as the activation gradient of Neuron 2, and analyzes the impact of its gain on the hyperbolic tangent activation function. Furthermore, a discrete magnetically controlled memristor model combined with sine and square functions is used as subsynapses of Neuron 1, resulting in a Discrete Hopfield neural network (DHNN) model with infinitely many equilibrium points. The results indicate that this DHNN can exhibit complex and diverse dynamical behaviors related to multiple system parameters, including rare hyperchaotic behavior characterized by four positive Lyapunov exponents, discharge mode transitions, and coexistence phenomena. Based on this, an efficient and secure color image encryption scheme is designed, and extensive security analysis shows that the proposed scheme has strong security performance.
Enhancing Saccharomyces cerevisiae alcohol production by Discoloring red sorghum (Sorghum bicolor L. Moench) malt: use of Gompertz 3-parameters Model
Authors: Amisi Arthur Kapepa , Bwanganga Jean-Claude T.
Published: October 2024

The fermentation of sorghum wort is one of the fundamental steps that determine the quality of this beer. It is influenced by several parameters, among others: temperature, pH, sugar content, amino acid profile, phenolic compound content, and redox potential. Controlling these parameters will therefore make it possible to optimize the production of alcohol and the growth of the yeast during fermentation. Three parameters were considered in this work, namely: The polyphenol content in the grain (Discolored and non-discolored sorghum), temperature (10 and 25°C) and fermentation time (in hours). The results obtained give a better alcohol content of 4.8% for the E1T1 (Discolored sorghum, fermentation at 10°C). The comparison of the means confirms that, and simply means that the extraction of the phenolic compounds before malting as well as the fermentation temperature had a significant effect on the production of alcohol and the growth of the yeast. The results of this work open the door to many other studies to provide to the industrial and artisanal brewers with scientific data that will allow them to better integrate red sorghum malt as raw material into brewing.
Exploring near-infrared spectroscopy ability to predict the age and species of An. gambiae mosquitoes from different environmental conditions in Burkina Faso
Authors: Djègbè Nicaise D. C. , Da Dari F. , Somé Bernard M. , Paré Lawata Inès G. , Cissé Fatoumata , Kaboré Jacques , Churcher Thomas S. , Dabiré Roch K.
Published: October 2024

Background
Near infrared spectroscopy (NIRS) showed ability to predict some important entomological parameters in laboratory-reared and wild mosquitoes with moderate to high accuracy. Before validating the technique as a routine tool, it is necessary to assess NIRS accuracy on these variables under different environmental conditions similar to natural setting, as temperature and humidity could impact the mosquito cuticle and interfere with the machine prediction. This study aims to investigate the influence of environmental conditions on NIRS accuracy to determine age and species of An. gambiae s.l.
Methods
Environmental conditions of three important seasonal periods in Burkina Faso covering the onset, the peak and the end of the rainy season were mimicked in the laboratory using incubators. Emerged An. gambiae and An. coluzzii from laboratory colony were reared in each environmental condition and analysed by NIRS to predict mosquito species. Wild An. gambiae s.l. were caught during the 3 different periods described above and analysed by NIRS to compare the two results. Furthermore, first generation of Anopheles was used to assess NIRS ability to determine mosquito age in each environmental condition.
Results
NIRS discriminated between laboratory-reared Anopheles with 83% of accuracy independently of any environmental condition. Similar trend was found in wild-caught Anopheles. NIRS prediction accuracies varied slightly in laboratory Anopheles (77% − 85%) and more strongly in their field counterparts (67% − 84%). In both cases, prediction models developed from the season of interest were a little more accurate than models trained with insectary conditions or from a different period of the year, indicating temperature and humidity can impact NIRS accuracy. Models derived from laboratory-mosquitoes reared under fluctuating environmental conditions predicted field-derived mosquito species with a low accuracy (59%). Models trained on smaller datasets and varying conditions were reliably classified age into two categories (< 9 days or ≥ 9 days, 79% − 84% accuracy).
Conclusion
NIRS was able to predict An. gambiae s.l. species and classified age into two categories under different environmental conditions with modest accuracy. Models trained using wild mosquitoes from one season could predict species in wild mosquitoes from a different season, though with slightly lower accuracy.
Optimizing Timing and Preparation for Allogeneic Hematopoietic Stem Cell Transplantation in Higher-Risk Myelodysplastic Syndromes
Authors: SUN ZIMIN , Geng Liangquan , Chen Erling , JI YANPING , Liu Huilan
Published: October 2024

This retrospective study evaluated allo-HSCT outcomes in 70 higher-risk MDS patients classified by the IPSS-R. Key factors analyzed included the interval from diagnosis to allo-HSCT (early: <6 months vs. late: ≥6 months), number of prior treatment cycles (less: <2 vs. more: ≥2), remission status (CR/PR vs. NR), and donor type (sibling vs. unrelated cord blood [UCB]). The results showed a significantly higher 3-year OS in the early HSCT group (70% vs. 50%, P = 0.05) with lower TRM (22.7% vs. 46.5%, P = 0.0205). Although more pre-transplant treatment cycles were linked to a lower relapse rate (2.3% vs. 15.4%, P = 0.0403), they did not significantly affect OS or TRM. Early HSCT emerged as the only significant factor influencing both OS (HR 2.84, P = 0.01) and TRM (HR 3.21, P = 0.01). While no significant differences were noted between sibling HSCT and UCB for OS and TRM, UCB demonstrated a lower incidence of chronic GVHD (19.0% vs. 52.9%, P = 0.003). Early allo-HSCT is recommended, with UCB as a viable alternative to sibling donors.
Validation of new, circulating biomarkers for gliomas
Authors: Chatanaka Miyo K. , Avery Lisa M. , Diamandis Eleftherios P.
Published: October 2024

Background: Biomarkers are useful clinical tools but only a handful of them are used routinely for patient care. Despite intense efforts to discover new, clinically useful biomarkers, very few new circulating biomarkers were implemented in clinical practice in the last 40 years. This is mainly due to rather poor clinical performance.
Methods: Here, our goal was to validate the performance of a group of newly discovered circulating biomarkers for glioma by comparing our data with data from a paper recently published in Science Advances. We analyzed our own sets of clinical samples (gliomas (n=30), meningiomas (n=20)) and a different analytical assay (Proximity Extension Assay, OLINK Proteomics) to compare the results of Shen and colleagues.
Results: Despite the sophistication of the utilized discovery method by the original investigators, we found that the newly proposed biomarkers for glioma (the best one presumably being SERPINA6) did not perform as originally claimed.
Conclusions: Scientific irreproducibility has been extensively discussed in the literature. A large proportion of newly discovered candidate biomarkers likely represent “false discovery” and significantly contribute to the propagation of irreproducible results between investigators. One of the best ways to assess the value of any new biomarker is by independent and extensive validation. Based on our previous classification of irreproducible results, we believe that this new work likely represents another example of biomarker false discovery.
Study on the micro-motion wear mechanism of K417 alloy at high temperature under different normal load conditions
Authors: Jiang Tao , Dong Xinyan , Chen Hehe
Published: October 2024

Rim plate dry friction dampers have become increasingly popular in recent years as a simple and effective method of friction damping in various types of high-speed rotating machinery blades. This method improves the service life of turbine blades and enhances the safety and reliability of aero-engines by utilizing the friction between the damping material and the blade material to counteract the vibration energy generated. However, under different conditions, frictional wear can cause abnormal wear, material transfer, micro-crack generation, and expansion between the damping block and the blade. These issues may lead to premature failure of the component. This experiment utilized a cylindrical/planar line contact form for the rim-plate dry friction damper and blade contact parameters. Micro-motion wear experiments were conducted at high-temperature conditions in the SRV-V multifunctional friction and wear tester to simulate the contact state of the dry friction damper block and turbine blade under working conditions. The study investigated the micro-motion friction and wear behavior of K417 alloy under varying normal contact stress conditions. The results indicated an inverse relationship between the friction coefficient and the normal load in the same micro-motion environment. Additionally, the degree of fluctuation flattens out with an increase in load. The normal load increase caused the micro-motion operating region characteristics to transition from the complete slip zone to the mixed zone. In the complete slip zone, the micro-motion wear forms were mainly abrasive, fatigue, and oxidative wear, while in the mixed zone, they were mainly adhesive and oxidative wear. Under low load conditions, the bonding degree between the three-body layer and the substrate material was more effective, resulting in better substrate protection. However, under high load conditions, the increase in contact stress caused a decline in the bonding degree between the two, resulting in cracking and crushing of the three-body layer and loss of substrate protection, leading to gradual damage.
Using acoustic recorders and accounting for imperfect detection to understand spatial and temporal breeding patterns of a cryptic burrowing amphibian
Authors: Kelly Oliver , Beranek Chad , Gould John , Wallace Samantha , Stock Sarah , Klop-Toker Kaya , Mahony Stephen , Daly Garry , Stauber Andrew , Mahony Michael , Callen Alex
Published: October 2024

Understanding the determinants of detection and occupancy at breeding habitats is crucial in the management of threatened amphibian species. This study investigates the spatial and temporal breeding patterns of the giant burrowing frog, Heleioporus australiacus australiacus, a cryptic and threatened amphibian species, through acoustic monitoring. We aimed to quantify the detectability of this species and identify environmental factors that influences its breeding behaviour. Passive acoustic recorders were deployed between May 2018 and November 2021 across eastern NSW. From 5,030 survey nights, the giant burrowing frog was only detected calling on 44 occasions. The estimated probability of detecting a giant burrowing frog during a single survey was p̂ = 0.01, confirming the cryptic nature of its breeding behaviour. Our analysis revealed that calling behaviour in the giant burrowing frog is correlated with climatic variables, with increased detection probabilities associated to higher nightly temperatures and impending rainfall. Conversely, higher daily rainfall and solar radiation levels were associated with a decrease in detection probability. Temperature, precipitation, seasonality and habitat aspect were identified as key factors influencing breeding site occupancy. Overall, our study highlights the importance of accounting for imperfect detection in monitoring programs and provides valuable insight into the breeding behaviour of a difficult to detect species. These findings have implications for the management and conservation of giant burrowing frog populations, particularly in the face of changing environmental conditions. Continued monitoring efforts, focusing on tadpole surveys and passive acoustic monitoring, are recommended to improve our understanding of their ecology.
Investigation of Sleeve Flange Connection of Cold Formed Steel Channel Under Bending Test
Authors: R. Muhammad Alif I. , F. Kamarudin A. , Hakim Seyed , Dtauwel S. J. Mohamad , Faisal M. N. Ade
Published: October 2024

Cold-formed steel (CFS) is gaining popularity due to its lightweight construction, high strength, and sustainability. CFS is produced without heat, and it is utilized in construction, automobiles, and appliances. Sleeve flange (SF) connections are commonly used in civil and structural constructions like trusses, wall panels, flooring, etc. involving a sleeve that is used to connect over two CFS members by screwing them together. However, lack of engineering evident was focusing on this type of connection method previously. In this study, the bending performance of a 75 mm (D) x 35 mm (B) x 7 mm (S) lipped CFS C-section sleeve flange connection that is connected by M5.5 mm-diameter self-drilling screws with 20 mm length was investigated. Two main variables were considered for the laboratory testing which were the thickness and the number of screws. At the initial stage, the mechanical properties of the CFS C-section and screw fasteners were determined according to the ASTM E8 laboratory testing procedure. Twelve samples with CFS C-section thicknesses of 0.75 mm and 1.00 mm connected by two and four numbers of screws underwent a four-point bending test using 100 tn of Universal Testing Machine. The ultimate load (Pu) and failure mechanisms were investigated. From the laboratory finding, it was found that 1.00 mm thickness of CFS C-section samples was able to increase the minimum of 1.5 times of the ultimate moment against the 0.75 mm thickness for both two and four screw type of connections. Besides, the analytical calculations of moment resistance and buckling resistance were also carried out based on EC3 EN1993-1-3 and EC3 EN 1993-1-1 for comparison with the laboratory outcomes. The percentage of discrepancy between both analytical and experimental is inconsistent between both variables. It must be emphasized that there must be enough screws, especially for 0.75 mm thickness of CFS C-section, in order to make sure the resistance on both moment and buckling is satisfactory. Finally, all samples experienced two modes of failure, which were lateral torsional buckling (LTB) and local buckling failures.
Improved Bispectrum Adaptive Power Adjustment Algorithm for Power Detection HPLC Channel Diagnosis
Authors: Sun Yongqiang , Li Jianbin , Han Yonglu , Zhang Zhi
Published: October 2024

In response to the frequent issues of signal power loss and poor channel balance in High Power Line Communication (HPLC), this study proposes an improved dual spectrum adaptive power regulation algorithm model. This algorithm uses the bi spectral quadratic feature model method to establish a sparse observation model. By constructing a signal parameter database, the communication signal processing problem is transformed into an estimation problem of signal motion parameters. By matching the digital information in the input-output HPLC signal data pairs, a set of IF-THEN rules is constructed, and an adaptive power regulation algorithm model is incorporated into the data information rules. The experiment shows that its communication delay reaches a minimum of 280ms, greatly improving the HPLC channel equalization capability.
Digital Divide in the Hospitality Industry: Perspective from Türkiye
Authors: Baltacı Furkan , Özlü Bünyamin
Published: October 2024

This study aims to address the variables that cause the digital divide in the hospitality industry within the framework of Technology Readiness (TR). In this context, a comparative analysis of Generations Y (GEN Y) and Z (GEN Z) was conducted. An online survey was conducted with 880 people living in Gaziantep/Türkiye who had previously experienced tourism. The data obtained was analyzed using SPSS and AMOS. As a result, it was determined that the TR levels of GEN Y and Z were different from each other. GEN Y evaluates technology as discomfort and insecurity. GEN Z focuses on the innovative and practical aspects. According to the Tri-reference point (TRP) theory, the condition for using technology for GEN Y individuals is to feel trust and comfort. GEN Z, on the other hand, does not pay as much attention to these two factors as GEN Y. The prerequisites for GEN Z are utilitarianism and innovation. According to TRP, security and comfort should be prioritized to direct GEN Y to usage behavior. For GEN Z, plans should be made to emphasize the innovative and utilitarian aspects.
Eggshell-Derived Particle Composites with Epoxy Resin for Enhanced Radiation Shielding Applications
Authors: Tochaikul Gunjanaporn , Moonkum Nutthapong
Published: October 2024

This study explores the development and efficacy of eggshell-derived particle composites with epoxy resin for enhanced radiation shielding applications. Eggshells, predominantly composed of calcium carbonate were processed into particles of three distinct sizes: small, medium, and large. These particles were integrated into epoxy resin at a 50% weight ratio and characterized using a Laser Particle Size Distribution Analyzer. The mechanical properties and radiation shielding effectiveness of the composites were rigorously tested. Composites with large particles showed the weakest mechanical performance, with a maximum force of 5,674 N and stress of 52 MPa. Notably, small particle composites demonstrated superior results among the composites, with a maximum force of 9,125 N and stress of 97 MPa. Regarding radiation shielding properties, the small particle composites were the most effective, achieving the highest linear attenuation coefficient and the greatest reduction in radiation dose. This superior performance is attributed to the uniform dispersion and increased surface area of the smaller particles, which enhance radiation attenuation. These findings highlight the potential of using finely ground eggshell particles to create cost-effective, environmentally friendly materials for radiation protection, underscoring the importance of particle size optimization in the development of advanced composite materials.
Structural cohesion in online Web-based expertise-sharing communities
Authors: Perekhodko Eugenia
Published: October 2024

In this paper, we take a network-analytic approach to analyzing structural cohesion in online expertise-sharing communities. We investigate how the densification of structural cohesion inside online expertise-sharing communities is connected with the expertise propagation within the communities. Our findings show that increased structural cohesion is associated with increased levels of expertise in online expertise-sharing communities. We demonstrate the distribution of the experts within the first and second levels of cohesion of several online expertise-sharing communities and show how the most valuable experts in online expertise-sharing communities are distributed. Our findings enhance online communities with the theory of structural cohesion in online expertise-sharing communities and help to locate the experts.
Senegalese laterites for the treatment of arsenic-contaminated mining waters
Authors: DIOP Tidiane , Anne Gaudin , Lebeau Thierry
Published: October 2024

Arsenic contamination of water is a global issue with significant health consequences in many countries. Several treatment solutions exist, but they are often costly. In this study, the adsorption of arsenic was tested using two natural laterites from Eastern Senegal (Massawa (MASS) and Sabodala (SAB) locations). Their mineralogy consists of kaolinite (23-40%), quartz (23-39%), hematite (23-16%), and goethite (5-8%). Several conditions of arsenic adsorption by these two laterites were tested using batch experiments, including contact time, pH, and the ratio between the mass of laterite and the volume of arsenic-contaminated solution. Results indicate high removal rates of arsenic over a wide range of pH conditions (2-7, 8) for both laterites. For a laterite mass/water volume ratio of 0.1 g/50 ml, arsenic sorption reaches equilibrium within 60 minutes of contact time for MASS, whereas this time is between 60 and 120 minutes for SAB. A volume of 50 ml of arsenic-contaminated solution containing 5.69 mg/l of arsenic (V) is treated in 60 minutes with 1.5 g of both laterites. This short equilibrium time, compared to many studies reported on laterites worldwide, allows for higher water throughput, making the treatment of arsenic-contaminated water more efficient. The adsorption kinetics are slow and follow the pseudo-second-order model, suggesting the presence of chemisorption (inner-sphere adsorption) for both laterites. These results provide valuable insights into the practical suitability of Senegalese laterites for treating mine waters, particularly in the context of Senegal.
DI-AODV Model with Sustainable Data Management Scheme to Improve Performance and Data Accessibility in VANET
Authors: Mahajan Poonam M. , Patil Gouri M.
Published: October 2024

Cooperation and coordination between mobile nodes (vehicles) and static nodes (roadside infrastructure) are crucial in vehicular ad hoc networks (VANETs). Rapid topological modifications and the perception and transmission of physical environment parameters are the main problems of VANETs. Effective routing is a difficult problem in VANETs because to the vehicles' selfishness and the absence of a central coordinator. Effective routing requires optimal resource allocation, and this work suggested the DI-AODV model to boost VANET performance. In DI-AODV model, AODV protocol is modified by using Dijkstra Shortest Path Algorithm. Then, Data Replication method which is Sustainable Data Management Scheme is provided to ensure data delivery. The experimental result shows the proposed DI-AODV model provides better performance compared with other existing protocols in VANET.
EFFICIENT REMOVAL OF NORFLOXACIN FROM WATER BY Fe-MOFs
Authors: Wei Fuhua , Ren Qinhui , Wang Nannan , Chen Hongliang , Zhang Yutao , Liang Zhao
Published: October 2024

The synthesis of Fe-MOFs (metal-organic frameworks) was conducted using a solution heat method, followed by characterization through XRD (X-ray diffraction), SEM (scanning electron microscopy), and TG (thermogravimetry) analysis techniques. Subsequently, the Fe-MOFs were employed for norfloxacin removal. The experimental findings demonstrate that theoretical simulations of norfloxacin adsorption by Fe-MOFs involved the application of pseudo-first kinetic model, pseudo-second kinetic model, and adsorption isotherm. Chemisorption was identified as the primary mechanism observed. Notably, a maximum adsorption capacity of 197.7 mg g–1 was achieved at pH 10. These results underscore the practical feasibility of utilizing Fe-MOFs in applications related to norfloxacin adsorption.
HyperArc jako nowoczesna metoda radiochirurgii stereotaktycznej leczenia zmian wewnątrzczaszkowych
Authors: Kijeska Weronika , Graczyk Kinga , Skrobała Agnieszka
Published: October 2024

Single-fraction SRS stereotactic radiosurgery is commonly used for brain tumors and intracranial metastases. It uses high-dose ionizing radiation while sparing healthy tissues. The aim of this study was to present and analyze the modern HyperArc stereotactic technique implemented on a classic linear accelerator with an MLC multileaf collimator. HyperArc is a special type of VMAT  (Volumetric modulated arc therapy) technique that is an alternative solution to stereotactic radiosurgery (SRS). It enables the use of the isocentric technique for the simultaneous irradiation of many tumors in the brain area on a classic linear accelerator with a multileaf MLC collimator and also allows you to set different dose values ​​for individual tumors in one treatment plan. Through  optimization, we avoid high doses in healthy tissue and between target volumes. Compared to other stereotactic techniques, it enables a much shorter irradiation time, similar to the treatment time in dynamic VMAT or IMRT Intensity modulated radiation therapy) techniques.
Metody kompensacji ruchomości oddechowej
Authors: Kijeska Weronika , Kosmowska Justyna , Świątek Katarzyna , Skrobała Agnieszka
Published: October 2024

The motion of the patient's anatomical structures may affect the accuracy of its delivery. There are different methods of dealing with organ movement while delivering treatment. The aim of the study  was to review solutions dealing with the respiratory motions of patients. Compensation of this motion during irradiation with external beams and control and monitoring systems have been presented and discussed. In addition to the methods used on classical linear accelerators, the respiratory tracking technique used on the CyberKnife was presented.
Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques
Authors: Mevada Niyati , Srivastava Rohit
Published: October 2024

Cloud properties are pivotal in analyzing rainfall patterns in monsoon-dependent countries such as India. The impact of climate change becomes more important in regions susceptible to hydrometeorological events due to different monsoon regimes. To examine regional heterogeneity of cloud properties, this study investigates long-term trends and predictive capabilities for cloud properties in drought- and flood-prone regions of western India, utilizing satellite data and employing machine learning (ML) models to comprehend intricate data patterns and enhance predictive accuracy. The results show higher mean and variability in cloud parameters over the flood-prone area due to favorable rain conditions, reflecting higher cloud microphysical and optical properties. These parameters negatively correlate with some cloud macrophysical properties and aerosol property in the drought-prone area. A moderate correlation exists between certain cloud characteristics of one region and another. Employing ML for regression analysis and comparing them for cloud effective radius across regions shows promising results, with random forest demonstrating high coefficient of determination (0.86, 0.93) and low root mean squared error (0.76, 1.15) due to its robustness and high accuracy. This research enhances the understanding of regional heterogeneity in India and shows that ML can help in predicting future cloud dynamics and climate by suitable model.
Confirmatory factor analysis for the implementation of Green Lean Six Sigma (GLSS) in Malaysia's wastewater treatment industry
Authors: Ishak Anuar , Mohamad Effendi , Rahman Muhammad Arfauz A.
Published: October 2024

This study investigates the critical enablers influencing the implementation of Green Lean Six Sigma (GLSS) in Malaysia's wastewater treatment industry. Through an extensive literature review and insights from the industry, 30 distinct enablers were identified and categorized based on their characteristics within wastewater treatment plant (WWTP) organizations, aimed at ensuring successful GLSS execution. Structural equation modelling was employed to validate the research model, utilizing data from 296 certified professionals in Malaysia. The analysis revealed five significant enablers, indicating moderate to high levels of GLSS adoption within the industry, with the ‘strategic’ and ‘resource’ enablers emerging as particularly influential factors. Subsequent confirmatory factor analysis further affirmed the validity and reliability of these enablers. Moreover, the findings demonstrated both convergent and discriminant validity, reinforcing the efficacy of these factors in measuring GLSS implementation in Malaysian WWTPs. The study highlights the critical importance of strategic planning and resource allocation while emphasizing the need to address cultural and environmental factors for successful GLSS adoption in the industry. However, enablers based on linkages, particularly those pertaining to supplier relationships and customer satisfaction, garnered the least consensus among respondents, indicating areas necessitating further attention and improvement.
Boosting BOD/COD biodegradability of automobile service stations wastewater by electrocoagulation
Authors: Herrera Luis , Sigcha Pavlova , Banchón Carlos
Published: October 2024

This study investigates the application of electrocoagulation for enhancing the biodegradability of organic matter in automobile service station wastewater, notorious for its contamination with polyaromatic hydrocarbons, heavy metals, and surfactants. Optimization of key variables such as electrode material, current density, and electrical consumption is conducted, with correlation analysis assessing their impact on water quality. Experimental setups utilize a vertical configuration comprising eight monopolar steel electrode plates as cathodes and eight counter electrodes (either iron or aluminum) connected in parallel. Results indicate that employing iron as the sacrificial electrode significantly increases the biochemical oxygen demand/chemical oxygen demand (BOD/COD) ratio, highlighting the efficacy of heightened power levels in enhancing organic matter degradation. Optimal removal efficiencies, including 87.5 for COD, 96.01 for BOD, and 92.2% for total solids, are achieved at a current density of 42 A/m2 and energy consumption of 360 kWh/m3, while maintaining pH levels between 6 and 9. The findings underscore the potential of electrocoagulation with Fe, Al as anodes, and stainless-steel cathodes as an efficient wastewater treatment approach, particularly for COD, BOD, and solid particle removal, thus contributing significantly to environmental sustainability.
Design, Synthesis, Molecular Docking, Pharmacokinetic Properties, and Molecular Dynamics Simulation of Sulfonyl Derivatives of Benzimidazole against Parkinson’s Disease
Authors: Roy Subarna , Basak Subhankar , Roy Shristi , Dey Paromita , Barman Hema , Singh Bhagat , Sarkar Kaushik , Sen Subhadeep , Das Rajesh Kumar , Debnath Sudhan , Biswas Goutam
Published: October 2024

Introduction:

The disability and mortality related to Parkinson's disease (PD), a
neurodegenerative disease, are increasing globally at a faster rate than other neurological disorders.
With no permanent cure for PD, there is an urgent need to develop novel and effective
anti-PD drugs.

Method:

Targeting monoamine oxidases (MAO), which catalyze the breakdown of
neurotransmitters, is one way to treat neurodegenerative diseases. In this context, an initial molecular
docking of twenty designed sulfonyl derivatives of benzimidazole against monoamine
oxidase B (MAO-B) associated with PD was conducted using AutoDock Vina.

Result:

The results were compared with those of the conventional inhibitors, selegiline and
rasagiline. Based on the docking score, the in-silico pharmacokinetic properties (ADME),
drug-likeness, and toxicity profiles of the newly synthesized molecules were examined using
SwissADME, PreADMET, ProTox-3.0, vNN, and ADMETlab web tools. Then, twelve potential
derivatives were synthesized and characterized by IR, 1H-NMR, 13C-NMR, 19F-NMR
(for some compounds), and mass spectrometry. Derivatives 2cj and 1bj were the two molecules
having the best binding affinity of -11.9 and -11.8 kcal/mol, respectively, against MAOB,
exhibiting a higher binding affinity compared to that of some commercially available drugs.
A 50 ns MD simulation run was performed to observe the stability of the top two docked
complexes, MAO-B-2cj and MAO-B-1bj, in order to further validate the efficacy of those two
substances. Moreover, the MM-PBSA method was used to calculate the final, binding free energy
of the simulated (MAO-B-2cj) complex.

Conclusion:

This study indicates that the binding affinity of most of the hits was superior to
that of known MAO inhibitors; therefore, these newly synthesized benzimidazole derivatives
may be developed into essential drug candidates for the treatment of PD.
Distribution Of Microrna Counts Across Human Chromosomes
Authors: Wang Hsiuying
Published: October 2024

Introduction:

microRNAs (miRNAs) are a class of non-coding RNAs that
play important roles in gene regulation. miRNAs are transcribed from DNA sequences
into primary miRNAs and then processed into precursor miRNAs and mature miRNAs.
miRNA gene counts in chromosomes for different species have been studied.

Method:

Certain chromosomes have higher numbers of miRNA genes in all species,
such as the X chromosome, while the Y chromosome has the fewest or no miRNA
genes. miRNA counts in different chromosomes might have a positive correlation with
coding gene counts in many species. In this study, a regression model was used to find
the relationship between the miRNA count and the coding gene count across human
chromosomes, and miRNA counts for 23 human chromosomes were predicted based on
this regression model. In addition, the chromosome locations for the miRNA biomarkers
of major depression, diabetes, Parkinson’s disease, and COVID-19 are discussed.

Results:

The results reveal that miRNA biomarkers of these diseases are located in various
chromosomes. The dispersion of miRNA locations across different chromosomes
might explain the complication of the pathology of these diseases. Moreover, diabetes
and COVID-19 have the largest number of miRNA biomarkers from Chromosome X.

Conclusion:

As Chromosome X is a sex chromosome, this phenomenon may explain
the gender difference in the prevalence or severity of diabetes and COVID-19. The significant
gender difference in the prevalence or severity of diabetes and COVID-19 might
be due to the regulation function of their miRNA biomarkers from Chromosome X.
Machine Learning-based Macrophage Signature for Predicting Prognosis and Immunotherapy Benefits in Cholangiocarcinoma
Authors: Huang Junkai , Chen Yu , Tan Zhiguo , Song Yinghui , Chen Kang , Liu Sulai , Peng Chuang , Chen Xu
Published: October 2024

Aims:

We aimed to develop a macrophage signature for predicting clinical
outcomes and immunotherapy benefits in cholangiocarcinoma.

Background:

Macrophages are potent immune effector cells that can change phenotype
in different environments to exert anti-tumor and anti-tumor functions. The role of
macrophages in the prognosis and therapy benefits of cholangiocarcinoma was not fully
clarified.

Objective:

The objective of this study is to develop a prognostic model for cholangiocarcinoma.

Methods:

The macrophage-related signature (MRS) was developed using 10 machine
learning methods with TCGA, GSE89748 and GSE107943 datasets. Several indicators
(TIDE score, TMB score and MATH score) and two immunotherapy datasets (IMvigor210
and GSE91061) were used to investigate the performance of MRS in predicting
the benefits of immunotherapy.

Results:

The Lasso + CoxBoost method's MRS was considered a robust and stable model
that demonstrated good accuracy in predicting the clinical outcome of patients with
cholangiocarcinoma; the AUC of the 2-, 3-, and 4-year ROC curves in the TCGA dataset
were 0.965, 0.957, and 1.000. Moreover, MRS acted as an independent risk factor
for the clinical outcome of cholangiocarcinoma cases. Cholangiocarcinoma cases with
higher MRS scores are correlated with a higher TIDE score, higher tumor escape score,
higher MATH score, and lower TMB score. Further analysis suggested high MRS score
indicated a higher gene set score correlated with cancer-related hallmarks.

Conclusion:

With regard to cholangiocarcinoma, the current study created a machine
learning-based MRS that served as an indication for forecasting the prognosis and therapeutic
advantages of individual cases.
Clinical Characteristics and Outcomes in Tracheobronchopathia Osteochondroplastica: A Scoping Review
Authors: Nyanti Larry Ellee , Huan Nai Chien , Ramarmuty Hema Yamini , Ho Rong Lih , Lansing Meryl Grace , Chua Chia Zhen , Ng Wai Hoe , Khor Cheng Zhi , Low Jun Lyn , Mathew Ashwin , Cheah Kok Choon , Menon Saieeshwaran , Shanmugam Vikneshwara , Soe May Zoe , Kannan Kunji Kannan Sivaraman
Published: October 2024

Background and Objective:

Tracheobronchopathia osteochondroplastica (TBOCP) is a
rare, benign condition characterised by the presence of multiple bony and cartilaginous nodules in
the tracheal and bronchial submucosa. Its etiology remains unknown, and clinical presentations
and management vary.

Methods:

We conducted a scoping review to evaluate clinical and radiological characteristics, diagnostic
investigations and prognostic outcomes of TBOCP in adults by exploring Pubmed and
SCOPUS databases.

Results:

From the search results of 675 studies, 98 were included in this scoping review. TBOCP
has a myriad of presenting symptoms and clinical severity but is notably an asthma mimic, displaying
an obstructive pattern on spirometry. Bronchoscopic tools were the most common interventional
diagnostic or management tool.

Conclusion:

While the etiology of TBOCP remains unknown, clinicians are alerted to its varied
presentations and classical histopathological and gross morphological appearance.
Identification of Volatile Organic Compounds (Distinct Biomarkers) Emitted by Cancer Cells Using Gas Chromatography-mass Spectrometry – A Review
Authors: Balu Surendar , Sundramoorthy Ashok K.
Published: October 2024

The utilization of Gas Chromatography-Mass Spectrometry (GC-MS) for the detection of
Volatile Organic Compounds (VOCs) as potential biomarkers in cancer diagnostics is an evolving
and promising field. This review outlines current exploratory and pilot studies that lay a fundamental
groundwork for future research. Due to the complexity of the fecal and respiratory volatilome, influenced
by a myriad of biological and environmental factors, it is crucial to expand research demographics
to enhance data robustness and ensure the applicability of findings across diverse populations.
We have also, herein, highlighted the dual capability of VOC analysis to detect cancer and
differentiate between its stages, which is vital for customizing patient treatment plans and monitoring
therapeutic efficacy. Furthermore, establishing quantifiable thresholds for VOC concentrations is
essential for their clinical adoption as reliable indicators of cancer. The integration of GC-MS with
machine learning and Artificial Intelligence (AI) could be useful for comprehensive metabolic profiles
and facilitating the development of non-invasive and real-time diagnostic tools. The adoption of
multi-omics approach promises a deeper understanding of cancer biology, potentially leading to personalized
medicine and strategies finely tuned to individual molecular profiles. Hence, the research
on VOCs as cancer biomarkers is set to offer transformative advancements in diagnostic technologies,
ultimately improving patient outcomes through tailored therapeutic interventions.
Developing a New Method for the Estimation of Valsartan (VST) in Pharmaceutical Dosage Samples by Using Diazotization and Coupling Reactions Spectrophotometrically
Authors: Pasha Chand
Published: October 2024

Introduction:

A new method for valsartan (VST) estimation in pharmaceutical dosage samples by spectrophotometry was developed.

Method:

The method was based on the formation of coloured dye by the diazotization reaction of 3-amino-2-naphthol with sodium nitrite in an acidic medium to form diazonium compounds, which were then coupled with VST in a basic medium.

Results:

The drug sample showed linearity in the range 4.6 - 24.2 μgmL-1, and the λmax was found at 432nm. Sandell’s sensitivity (9.950×10-3), Molar absorbtivity (Ɛ= 4.377×104), regression equa-tion (y= 0.0775x + 0.0041), correlation coefficient (r2= 0.968), detection limit (DL= 0.668) and quantitation limit (QL= 2.024) were evaluated.

Conclusion:

The percentage recovery of the drug samples was found to be 100%. This method successfully determined VST in Pharmaceutical dosage samples.
Significance of IoT for Smart Homes and Cities
Authors: Bhardwaj Akashdeep
Published: October 2024

The integration of the Internet of Things (IoT) has ushered in a
transformative era for both residential environments and urban landscapes, re-defining
the way people live, work, and interact within them. This chapter delves into the
profound significance of IoT in the realm of smart homes and cities, exploring the
multifaceted impact it has on enhancing efficiency, sustainability, and quality of life. In
the context of smart homes, IoT technology seamlessly intertwines household devices,
appliances, and systems, creating a networked ecosystem that enables automation,
remote control, and intelligent decision-making. This interconnectivity offers residents
unprecedented levels of convenience, energy efficiency, and security while paving the
way for innovative services like predictive maintenance and health monitoring.
Extending the scope to smart cities, this chapter explains how IoT transforms urban
environments into dynamic, data-driven entities. Through an intricate web of sensors,
actuators, and data analytics, cities can optimize resource allocation, traffic
management, waste disposal, and energy consumption. This leads to reduced
congestion, improved air quality, and a more sustainable urban infrastructure. However,
the integration of IoT into the fabric of smart homes and cities also raises significant
challenges pertaining to data privacy, security, and interoperability. These complexities
necessitate robust governance frameworks and technological solutions to ensure the
responsible and secure implementation of IoT technologies.
Secure Framework against Cyberattacks on Cyber-Physical Robotic Systems
Authors: Bhardwaj Akashdeep
Published: October 2024

Robot-based platforms and processes have integrated the security and
efficiency of data into a comprehensive range of domains like manufacturing, industrial,
logistical, agricultural, healthcare, and Internet services. Smart cyberattacks have been
on the rise, specifically targeting corporate and industrial robotic systems. These
attacks are executed once the IoT, Internet, and organization integration is implemented
with the industrial units. The authors implemented security criteria-based indices for
Cyber-Physical systems (CPS) with industrial components and embedded sensors that
process the information logs and processes. The authors proposed an attack tree-based
secure framework that does not include every CPS device; however, it takes into
consideration the critical exploitable vulnerabilities to execute the attacks. The authors
categorized each physical device and integrated sensors based on logs and information
in a sensor indices device library. This research simulated the real-time exploitation of
vulnerabilities in CPS robotic systems using the proposed framework in the form of a
two-phased process. This validates the enhanced data security output of the integrated
sensor and physical nodes with the intelligent monitor and controller system health
monitor during real-time cyberattacks. This research simulated common cyberattacks
on cyber-physical controller servers based on cross-site scripting and telnet pivoting.
The authors gathered known and unknown vulnerabilities and exploited them with a
tree-based attack algorithm. The authors calculated the average time for cyberattackers
with different skills when trying to compromise CPS devices and systems.
All-Inclusive Attack Taxonomy and IoT Security Framework
Authors: Bhardwaj Akashdeep
Published: October 2024

In the early 2000s, the Internet meant being able to connect different
communication devices, whereas the focus in the last few years has been on connecting
‘things’ to the Internet. Although there is no distinct classification for these devices and
things on the Internet, the Internet of Things (IoT) ecosystem primarily consists of a
complex network of devices, sensors, and things. These ‘things’ are controlled by
humans and utilize the existing cloud infrastructure. These devices provide facilities
and benefits to make our lives comfortable. IoT domains include smart homes,
healthcare, manufacturing, smart wearables, smart cities, smart grids, industrial IoT,
connected vehicles, and smart retail. Different IoT models involve human-to-IoT, IoTto-IoT, and IoT-to-traditional system architectures. In most scenarios, the architecture
ends up connecting to the unsecured Internet. This has thrown open several critical
issues leading to cybersecurity attacks on IoT devices. IoT communications, protocols,
or architecture have never been conceptualized to handle the new age of cybersecurity
attacks. IoT devices have limited computing, storage, networks, and memory. In this
research, the authors present a unique IoT attack framework named IAF, focusing on
the impact of IoT attacks on IoT applications and service levels. The authors also
propose an all-inclusive attack taxonomy classifying various attacks on IoT ecosystems.