Recently, various research has been developed addressing communication sensors for Unmanned Aerial Systems (UASs); especially when pondering control difficulties, communication is a crucial component. To this end, strengthening a control algorithm with redundant linking sensors ensures the overall system works accurately even if some components fail. This paper proposes a novel approach to integrate several sensors and actuators for a heavy Unmanned Aerial Vehicle (UAV). Additionally, a cutting-edge Robust Thrust Vectoring Control (RTVC) technique is designed to control various communicative modules during a flying mission and converge the attitude system to stability. The results of the study demonstrate that even though RTVC is not frequently utilized, it works as well as cascade PID controllers, particularly for multi-rotors with mounted flaps, and could be perfectly functional in UAVs powered by thermal engines to increase the autonomy since the propellers cannot be used as controller surfaces.

Surveys on the optimum seismic design of structures reveal that many investigations focus on how to minimize its initial cost while satisfying the performance constraints. Although reducing the initial cost while complying with the requirements of earthquake design codes of practice will significantly guarantee the life safety of occupants against earthquakes, it may still cause considerable economic losses to residents and fatalities. Accordingly, calculating the possible earthquake damages caused during the structure's lifetime may become essential from the optimal Life Cycle Cost (LCC) design point of view. LCC analysis is used to evaluate economic feasibility including construction, operation, occupancy, maintenance and end-of-life costs. For optimization, the population-based, meta-heuristic algorithm based on the Ideal Gas Molecular Movement (IGMM), has established its capability in solving highly nonlinear mono and multi-objective engineering problems. Therefore, this paper investigates the LCC -based mono and multi-objective optimum design of a 3-D four-story concrete building structure combined with the Endurance Time (ET) competence. The results show that, apart from substantially reducing the total number of analyses required, the proposed technique significantly affected dipping minor injury cost, rental, and income costs by 22%, 16%, and 16%, respectively. In total, a 10% reduction in all structural life-cycle costs was achieved, which could well be considered significant.

Pounded yam eaten with any type of soup is a delicacy eaten by Nigerians. It is manually prepared by pounding yam, cocoyam or combination with mortar and pestle. Preparation takes 30-45 mins. Researches on the improvements have taken place to reduce the heavy burden on the pounded yam preparation. One of the efforts made is the fabrication of pounding machine from the Department of Engineering, Federal College of Agriculture, Akure, Nigeria. The aim of this study is to carry out the organoleptic analysis of pounded yam produced from the fabricated machine. A ten-man panelist was used to determine the organoleptic analysis of the pounded yam in terms of the appearance, taste, colour, odour and texture. The panelists were scored with a class of quality grading: 22.6-25.0 (excellent), 20.2-22.5 (good), 18.2-20.1 (satisfactory), 16.2-18.1 (less satisfactory), and <15.2 (unsatisfactory). Also, sensory attributes were scored as 1= unsatisfactory, 2= less satisfactory, 3= satisfactory, 4=good, and 5= excellent. The results were statistically analysed. From the results it was found that the mean results of appearance, colour, odour, taste, and texture were 4.55, 4.43, 4.15, 4.32, and 4.08 respectively. Coefficients of variation (%) were 16.89, 17.80, 21.65, 15.67, and 24.40. The Skewness of the parameters was in negative, while only odour has its Kurtosis in negative form (-0.37). All the panelists rated the appearance (19-24%) of all the samples high above all other parameters. The Pearson Coefficient of Correlation and Principal Component Analysis showed elements of relationships between the parameters. In conclusion, the machine pounded yam was scored above satisfactory, which means the fabricated machine was efficient.

This study presents a groundbreaking approach in the field of gaming AI, focusing on the classic game” Pacman” through the lens of DRL integrated with advanced optimization techniques. The core of the research involves adapting DRL models for” Pacman”, utilizing the ESO for hyper- parameter tuning. These novel adaptations significantly enhance the AI agent’s performance, demonstrating a remarkable improvement in adaptability, responsiveness, and efficiency within the game environment. A pivotal aspect of this research is the innovative integration of metaheuristic optimization techniques into the DRL framework, a first in the domain of Atari gaming AI. This integration has proven essential in fine-tuning the DRL models, leading to a more effective and dynamic gaming experience. The study thoroughly evaluates and compares the performance of these algorithms, providing empirical support for their effectiveness and setting a new benchmark in AI-driven game development. The implications of this research extend beyond gaming AI, opening up possibilities for future exploration in several directions. Expanding DRL models to other complex gaming environments, continuous algorithmic enhancements, real-time learning adaptations, and applying these principles to robotics and autonomous vehicles are examples. The research also emphasizes ethical and responsible AI use in gaming to address fairness and addiction issues.

In the educational process, quality has long been considered a critical component. It may get impacted due to different factors especially when talking about the COVID19 pandemic. The purpose of this investigation is to explore the impact and relationship between COVID-19 and the quality of education using bibliometric analysis. As face-to-face learning shifts to online learning, this research investigates how the COVID-19 epidemic has affected education quality, concentrating on Programme Educational Outcomes (PEOs), Programme Learning Outcomes (PLOs), and Course Learning Outcomes (CLOs). The research covers pandemic online education, literature, and trends. There are two important steps in any bibliometric analysis selecting keywords and databases. The initial set of keywords was selected by the authors' experience and then an online survey study through Google Forms was conducted among experts in the research area. This was followed by a search of the selected keywords in the databases consisting of PubMed, Web of Science (WoS), and SCOPUS. Therefore, the final data was collected from the SCOPUS database on August 24, 2021. Hence, 182 documents were considered for the current study. The document's data were analysed by using the “visualization of similarities” (VOS) Viewer software and the R-Tool of the Bibliometrix-package that was specifically designed for quantitative Bibliometrics research. Based on the obtained data, the results showed the quality of education that may get impacted by the changing of the education delivery from face-to-face to online-based learning. In conclusion, this study showed the importance of studying the correlation and impact of COVID-19 on the quality of education.

Debates about the conditions in which the frequency and intensity principles of regular exercise, depending on the fact that sports background can be accepted as extremism, are still a controversial topic. The purpose of this research is to investigate the Exercise Dependence of Athletes doing Kickboxing, Taekwondo and Muaythai. The study included 141 athletes consisting of 87 men and 54 women. Exercise Dependence Scale composed of 21 items developed by Hausenblas and Downs and adapted into Turkish version by Yeltepe and İkizler was applied to athletes. As a result of the research, while athletes showed more sensitivity to exercise dependence scale (= 71.41), this scale was also defined as symptomatic. It was found that 5 athletes (3.5%) were asymptomatic-nondependent, 117 athletes (83.0%) were symptomatic-nondependent and 19 athletes (13.5%) were at-risk for exercise dependence. It was determined that athletes were at-risk for exercise dependence group while 8 athletes were doing kickboxing, 10 athletes were doing taekwondo and 1 athlete was doing muaythai athlete. A significant difference was observed according to regular training and number of daily training. It didn’t significantly differ in other variables. It is possible to say that regular training can be effective to reveal the exercise dependence.

Researchers, journals, and universities want to receive more citations for their scholarly publications. However, a paper citations depend on its quality, visibility and author’s online profile. Research support documents (unpublished papers, white papers, project reports, datasets, software, posters, online resources and teaching materials) can be additional source for increasing the author’s visibility. To enhance research visibility and impact, the full range of scholarly output should be available online on the open access platform. With open access platform, key research findings are made accessible immediately to the scientific community. Therefore, the publicly available research support documents may result in receiving more citations in addition to the published papers. In this paper we conducted a simplified three stages study on the benefits of research support documents publications on open access platform. We also elaborated on approaches of improving your research visibility and impact through these document.

The increasing demand for craft beer drives the search for novel ale yeast cultures from brew-ing-related wild environments. The focus of bioprospecting for craft cultures is to identify feral yeasts suitable to imprint unique sensorial attributes to the final product. Here, we integrated phylogenetic, genotypic, genetic, and metabolomic techniques to demonstrate that sour beer during aging in wooden barrel is a source of suitable craft ale yeast candidates. Differently from traditional lambic beer maturation phase, during aging of sour-matured production style beer, different biotypes of Saccharomyces cerevisiae dominated the cultivable in-house mycobiota, fol-lowed by Pichia membranifaciens, Brettanomyces bruxellensis, and Brettanomyces anomalus. In addi-tion, three putative S. cerevisiae x Saccharomyces uvarum hybrids were identified. S. cerevisiae feral strains sporulated, produced viable monosporic progenies, and have STA1 gene as well as the full-length promoter. During hopped wort fermentation four S. cerevisiae strains and the S. cere-visiae x S. uvarum hybrid WY213 overcame non-Saccharomyces strains in fermentative rate and ethanol production, except for P. membranifaciens WY122. This strain consumed maltose after a long lag phase, in contrast with the phenotypic profile descripted for the species. According to STA1+ genotype S. cerevisiae partially consumed dextrin, but no 4-vynil guaiacol was detected among VOCs, suggesting that these strains were phenolic off flavor-negative (POF-). In conclu-sion, the strains characterized here have relevant brewing properties and are exploitable as in-digenous craft beer starters.

A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental representation of concepts is structured along a simple branching tree spanned by superordinate and subordinate categories. We propose a generative model of item representation with correlations that overcomes the limitations of a tree structure. The items are generated through "factors" that represent semantic features or real-world attributes. The correlation between items has its source in the extent to which items share such factors and the strength of such factors: if many factors are balanced, correlations are overall low; whereas if a few factors dominate, they become strong. Our model allows for correlations that are neither trivial nor hierarchical, but may reproduce the general spectrum of correlations present in a data-set of nouns. We provide an estimate of the number of concepts that can be stored and retrieved by a large-scale cortical network, the Potts network, which is perhaps approximately 10⁷ with human cortical parameters. When this storage capacity is exceeded, however, retrieval fails completely only for balanced factors; above a critical degree of imbalance, a phase transition leads to a regime where the network still extracts considerable information about the cued item, even if not recovering its detailed representation: partial categorization seems to emerge spontaneously as a consequence of the dominance of particular factors, rather than being imposed ad hoc. We argue this to be a relevant model of semantic memory resilience in Tulving’s remember/know paradigms.

The development of numerical simulations is potentially useful in predicting the most suitable manufacturing process and ultimately improving product quality. Seamless pipes are manufactured by rotary piercing process in which round bars are fed between two rolls and pierced by a stationary plug. During this process, the material undergoes severe deformation which renders it impractical to be modelled and analysed with conventional finite element methods. In this paper, three dimensional numerical simulations of the piercing process are performed with Arbitrary Lagrangian-Eulerian (ALE) Formulation in LS DYNA software. Details about the material model as well as the elements formulations are elaborated here and mesh sensitivity analysis was performed. The results of the numerical simulations are in good agreement with experimental data found in the literature and the validity of the analysis method is confirmed. The effects of varying workpiece velocity, process temperature, and wall thickness on the maximum stress levels of the product material/pipes are investigated by performing simulations of sixty scenarios. Three dimensional surface plots are generated which can be utilized to predict the maximum stress value at any given combination of the three parameters.

Legacy risks from infrastructures and industrial installations often reveal themselves when a potential for failure has been discovered much later than at the stage of the design and construction of a structure. In which case, there might already be a problem with the legacy installation, or even a crisis, without having had an accident. When the hazard cannot be taken away, the question arises as to how much effort, if any, should be spent on improving the situation. The usefulness of the three archetypical approaches to this problem: setting a standard, the As Low As Reasonably Practicable approach and a case-by-case discourse approach are discussed for their applicability for these legacy risks. Although it would be desirable to retrofit legacy risks to previously set legal requirements as is the case when acceptability limits are set in law or demonstration of ALARP is demanded, it may be impossible to reduce the residual risk to an otherwise acceptable level without taking away or replacing the infrastructure, which is not acceptable either. Therefor in conclusion the only available solution to persistent legacy risk problems seems to be to have a thorough discussion with all relevant stakeholders until an agreement is in some way found.

Within education, we make assumptions the correct manner of teaching a student, and with that, we assume further that regardless of the intellectual content that a student should engage with, the acculturation for the customary (and always rather traditional) means of religious apprehension for the purpose of an assumedly universal and beneficial role. This article instead promotes the view that such determinations are (1) unsuitable for modern society and (2) causal to a disjunction with a truly proper manner of education in the form of the matheticism – a term rather unfortunately applied in educational theory.

This article introduces a new interpretation to Socialism that approaches it via a heuristic study of Epochs. It is intended by this study that the intention of socialism (what it politically, socially, et cetera is proclaimed to be) is to be proposed to be indeterminate.The expectation of what Socialism is to be as a future entity is merely an interpretation that is predicated on Marx’s Capitalist Laws that have been since shown to be false. Its argument is suggested significantly by Engels’ discussions of human development and therein places an added focus on a historically based platform.

This article considers a different application of similar methods that have been used to reject the Sapir-Whorf Hypothesis and expounds the idea as a member of the cumulative proof against the theory. Rather than to explicitly include models of generative grammars, this model instead does not violate the ideas of Optimality Theory – and therein it preserves such qualities as freedom of analysis while retaining the idea of the pertinent constraints. However, it is suggestive of momentary disagreements with the Innateness Hypothesis.

The modern Historicism is plagued by its non-scientific appropriation – rightfully so, if we consider the genocidal proclivities that have defined the post-modern era to act on a perceived justification for the historical process, then the philosophy is little more than a falsely asserted and criminal interpretation made by taxonomists and romanticists. Yet, it should be noted that the study retains significant value in the discussion of Epochs, but its pertinence is represented in degrees of unknowable ideas and uncertainties. This article introduces two new, pragmatic constituent studies of Historicism such that it is reformed in the context of indeterminacy and an expected, non-processional characterization becomes forefront to the study.

This article considers the status of Jungian archetypes as potentially divergent bodies. It possesses the initiative to consider the prerogative that they are situated on, and to develop that into discussing the conditions for a differing subjective reality that is derived from Jungian pretenses. This article operates within the boundaries of the conjecture, it serves simply to cover the potentiality for developments outside of the simple basis of the archetype rather than to provide a metaphysical or ontological contribution to psychology. Unlike Jung’s study, which serves a multitude of ideas and draws upon evidential factors from - however generally expressed - ancient civilisations, this article instead seeks to apply that logic for the purpose of explicating the meaning and potential extensions of the “archetypal dogma”.

This article is an early draft on a proposed alternative to the current Marxist mode of thought; one that seeks to reject the generally ill-conceived and dogmatic proposals formulated by Marx in favour of another predicated on the more unwavering emotional ideals of the philosophy that preceded it. It eliminates that dogmatic mode in favour of a wholly social society, one that is founded upon the Marxist superstructure and intends hence to capture that humanity and re-form particularly economic principles that are used to construct the utopia into what is instead an entirely socially idealised frame of living.

We study latching dynamics in the adaptive Potts model network, through numerical simulations with randomly and also weakly correlated patterns, and we focus on comparing its slowly and fast adapting regimes. A measure, Q, is used to quantify the quality of latching in the phase space spanned by the number of Potts states S, the number of connections per Potts unit C and the number of stored memory patterns p. We find narrow regions, or bands in phase space, where distinct pattern retrieval and duration of latching combine to yield the highest values of Q. The bands are confined by the storage capacity curve, for large p, and by the onset of finite latching, for low p. Inside the band, in the slowly adapting regime, we observe complex structured dynamics, with transitions at high crossover between correlated memory patterns; while away from the band latching transitions lose complexity in different ways: below, they are clear-cut but last so few steps as to span a transition matrix between states with few asymmetrical entries and limited entropy; while above, they tend to become random, with large entropy and bi-directional transition frequencies, but indistinguishable from noise. Extrapolating from the simulations, the band appears to scale almost quadratically in the p - S plane, and sublinearly in p - C. In the fast adapting regime the band scales similarly, and it can be made even wider and more robust, but transitions between anti-correlated patterns dominate latching dynamics. This suggest that slow and fast adaptation have to be integrated in a scenario for viable latching in a cortical system. The results for the slowly adapting regime, obtained with randomly correlated patterns, remain valid also for the case with correlated patterns, with just a simple shift in phase space.

(1) Background: Conventional strength training and core exercises are commonly prescribed to improve cycling performance. Although previous studies have explored the utility of strength training in various cycling populations, this intervention has never been compared to core exercises; (2) Methods: 36 trained road cyclists were divided in 3 groups of 12 participants that performed either no strength training, conventional strength training or core exercises, in all cases together with their regular cycling training during a 12-week period. Peak power outputs across different durations (5 seconds, 60 seconds, 5 minutes, and 20 minutes) were recorded before and after the intervention; (3) Results: Conventional strength training is superior to core training and no strength training for all measured durations. At the same time, core training results in improved 5 second power output when compared to no strength training, while all the other measures are comparable among groups; (4) Conclusions: Conventional strength training is superior to core exercises and no strength training in trained road cyclists, and accordingly, it is recommended that this population incorporates strength training during their regular weekly workouts.

Along with environmental pollutions, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, the consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and consequently, the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture.

The global predicament of solid waste generation looms as a mounting crisis, profoundly impacting the world, particularly in developing nations. Each day, the world's population contributes millions of tonnes of municipal solid waste, with projections pointing toward further escalation in the coming years. This surge in waste, predominantly composed of cellulose-rich materials, correlates directly with heightened pollution, a threat that transcends borders and imperils humanity as a whole.To confront this ever-growing issue, cellulase, a potent hydrolytic enzyme capable of digesting cellulose, a chief component of solid waste, is explored as a potential solution. In this study, we harness cellulase enzymes derived from the digestive tracts of both Archachatina marginata and Oryctes rhinoceros larvae, employing them as biodegradation agents for cellulose-based waste materials.The cellulase enzymes are methodically isolated and partially purified via ammonium sulfate precipitation at 65% saturation. Their efficacy in breaking down a variety of waste categories, encompassing kitchen waste, leaves, paper, wood, and industrial waste, is meticulously assessed and benchmarked against carboxymethyl cellulose (CMC), a well-established laboratory substrate. The study also ascertains the specific activity of these enzymes.Spectrophotometric analyses of the cellulase enzymes' actions on the diverse waste materials unveil intriguing results. Archachatina marginata and Oryctes rhinoceros larvae exhibit distinct performance metrics, registering percentage ranges of (11.04% - 28.31%) and (33.69% - 189.11%) on wood waste, (76.52% - 196%) and (108.56% - 567.01%) on paper waste, (78.57% - 148.14%) and (163.33% - 436.76%) on leaf waste, (57.16% - 179.95%) and (63.38% - 457.05%) on kitchen waste, and (59.95% - 77.51%) and (117.26% - 155.23%) on industrial waste, respectively. This study underscores the substantial biodegradation potential inherent in cellulase enzymes from both Oryctes rhinoceros larvae and Archachatina marginata when applied to diverse waste materials. While the rates and impacts of waste degradation vary between these sources, their promising contributions to waste management come into focus, offering sustainable and innovative approaches to tackle the pressing global challenge of solid waste proliferation.

Cancer is a leading cause of morbidity and mortality worldwide, yet much is still unknown about its mechanism of establishment and destruction. Recently, studies had shown that tumor cells cannot survive under the high temperature conditions. This treatment technique is called Hyperthermia. This report presents the case of radiative microwave heating of hyperthermia therapy on breast cancer in a porous medium. In this study, the steady state is solved analytically while unsteady state is solved using semi-implicit finite difference to get a more accurate prediction of blood temperature distributions within the breast tissues. A moderate temperature hyperthermia treatment is apply which results into cell death due to an increase in the level of cell sensitivity to radiation therapy and blood flow in tumor and oxygen. The results show that by applying metabolic heat generation rate of $3.97 X 10^{5}Wm^{-3}$, it takes upto 2 minutes for the tumor cells to get the require therapeutic temperature point.

In this paper, we address the following research question: Is feasibility to use an artificial intelligence system to predict the risk of student failure in a course based solely on their performance in prerequisite courses? Adopting a machine learning-based quantitative approach, we implement Course Prophet, the prototype of a predictive system that maps the input variables representing student performance to the target variable, i.e., the risk of course failure. We evaluate multiple machine learning methods and find that the Gaussian process with Matern kernel outperforms other methods, achieving the highest accuracy and a favorable trade-off between precision and recall. We conduct this research in the context of the students pursuing a Bachelor’s degree in Systems Engineering at the University of Córdoba, Colombia. In this context, we focus on predicting the risk of failing the Numerical Methods course. In conclusion, the main contribution of this research is the development of Course Prophet, providing an efficient and accurate tool for predicting student failure in the Numerical Methods course based on their academic history in prerequisite courses.

Multi-level direct networks fueled by the evolving technology of active optical cables and increasing pin-bandwidth achieve reduced diameters and cost, with high-radix switches. These networks, like Dragonfly, are becoming the preferred aspirants for extreme high-performance parallel machines such as exa-scale computing. In this paper, we introduce a Hyper Z-Tree topology, which deploys the Z-Tree, a variant of fat-tree, as a computing node of a Generalized Hypercube (GHC) configuration. The resulting configuration provides higher bisection bandwidth, lower latency for some applications and higher throughput. Furthermore, the levels of the fat tree offer several path diversities across the GHC dimensions, hence conceding a more fault tolerant architecture. To profit from these path diversities, we propose two adaptive routing algorithms, which are extensions to the routing algorithm suggested in HyperX topology. These two algorithms exhibit better latencies and throughput than the HyperX.

This article shows the alternative learning methodology to stimulate the holistic side of students’ minds to achieve the increment of the innovation skill, managing the creative competencies and level of stress. The present research study is pre-experimental research designed with prior and posterior measurement, longitudinal, explanatory, and co-relational, with the main objective to demonstrate the effect of the holistic innovation coefficient of the beneficiaries of the program “Impulsa Peru”. Program Results: It has been concluded that the experimental group is significant over the control group. Therefore, the holistic innovation methodology had an impact on the experimental group. Conclusions: Hypothesis 1 is fulfilled in which it is affirmed that the holistic innovation methodology has a positive impact on the level of coefficient of holistic innovation of the student mentors of the women recyclers of the program Impulsa Peru with a significance level of 0.05%.

This work proposes a hollow-core photonic crystal fiber-based edible oil sensor in the terahertz (THz) range (e.g., 1.0THz ≤ f ≤ 3.0THz) and different sensing characteristics are numerically analyzed. The suggested sensor’s performance was assessed by means of COMSOL Multiphysics, a commercial program that uses the finite element approach. The computational results indicate that the relative sensitivity is 85.591%, 84.648%, 82.625%, 82.683%, and 79.161%, respectively, at f = 2.2 THz, for several types of sunflower oil, mustard oil, coconut oil, olive oil and palm oil; and the corresponding effective areas are 7.22×10-8 um2, 7.09×10-8 um2, 6.83×10-8 um2,7.09×10-8 um2, 6.5231 ×10-8 um2. In addition, the effective material loss for sunflower oil, muster oil, coconut oil, olive oil, and palm oil has been found to be 0.02561 cm-1, 0.027054 cm-1, 0.030322 cm-1, 0.028854 cm-1 ,0.035427cm-1 respectively. Moreover, the proposed sensor also has low confinement loss are 1.55×10-8 dB/m, 1.63×10-8 dB/m, 1.31×10-8 dB/m, 1.99×10-8 dB/m, 4.0345×10-8 dB/m.This proposed sensor can be fabricated using extrusion and 3D-printing technologies, and due to its augmented detecting capabilities, it can be a vital part of oil sensing devices implemented in real life such as industry fields.

The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster-tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase, and a simple neural network for deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to im-prove tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and cost. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, the speed increased by 37.56%, and identity switches decreased by 46.81%.

This paper reports on the optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation systems for cattle farming in rural areas of Bangladesh. Bangladesh is a lower middle-income country with declining rates of poverty among its 160 million people due to persistent economic growth in conjunction with balanced agricultural improvements. Most of the rural households adopt a mixed farming system by cultivating crops and simultaneously rearing livestock. Among the animals raised, cattle are considered as the most valuable asset for the small/medium-scale farmers in terms of their meat and milk production. Currently, along with the major health issue, the COVID-19 pandemic is hindering the world’s economic growth and has thrust millions into unemployment; Bangladesh is also in this loop. However, natural disasters such as COVID-19 pandemic and floods, largely constrain rural smallholder cattle farmers from climbing out of their poverty. In particular, small and medium-scale cattle farmers face many issues that obstruct them from taking advantage of market opportunities and imposing a greater burden on their families and incomes. An appropriate measure can give a way to make those cattle farmers’ businesses both profitable and sustainable. Optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation system for cattle farming is a new and forward-looking approach for sustainable development of the livestock sector. In this study, we design and optimize a thin-film coating assisted hybrid (photovoltaic-battery-generator) power system by using the Hybrid Optimization of Multiple Energy Resources (HOMER, Version 3.14.0) simulation tool. An analysis of the results has suggested that the off-grid hybrid system is more feasible for small and medium-scale cattle farming systems with long-term sustainability to overcome the significant challenges faced by smallholder cattle farmers in Bangladesh.

Purpose: To improve numerical simulation of the non-contact tonometry test by using Arbitrary Eulerian-Lagrangian deforming mesh in the coupling between computational fluid dynamics model of an air jet and finite element model of the human eye. Methods: Computational fluid dynamics model simulated impingement of the air puff and consisted of 25920 wedge6 elements and employed Spallart-Allmaras model to simulate capture turbulence of the air jet. The time span of the jet wais 30 ms and maximum Reynolds number

Over the past decade, plasma waste treatment has become a more prominent technology due to increasing problems with waste disposal and realizing opportunities to generate valuable co-products. Plasma waste treatment extensively uses atmospheric pressure (RF) inductive coupled plasma (ICP) torches. In situ Optical Emission Spectroscopy (OES) is used to evaluate the developed RF ICP torch for Municipal Solid Waste (MSW) treatment and ascertain the plasma parameters to understand the physical mechanism involved. The argon plasma jet's electron temperature and plasma density outside the torch chamber are calculated using the Boltzmann plot and Stark broadening at different gas flow rates, and RF power. The expected electron temperature and plasma density behaviour were observed at a low gas flow rate. The electron temperature decreases with the RF power from 8089 K to 6097 K as demand for increasing the plasma density. An energy loss mechanism was revealed while raising the gas flow rate, as the electron temperature increases with RF power from 5750 K to 6221 K, and the plasma density decreases. This behaviour is due to the anomalous skin effect. Detecting and avoiding this phenomenon is essential as it negatively affects torch energy efficiency and waste treatment.

Bangladesh's railway system mostly uses typical manual railway crossing technique or boom gates through its 2,955.53 km rail route all over the country. The accidents are frequently happening in the railway crossings due to not having obstacle detectable and quickly operating gate systems, and also for fewer safety measures in the railway crossing. Currently, there are very few automatic railway crossing systems (without obstacle detectors) available, however, all of them are dependent on the national power grid without a backup plan for any emergency cases. Bangladesh is still running a bit behind in the power generation of its consumption, hence it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents in the railway crossing points. We design to use two infrared (IR) sensors to operate the railway crossing systems which will be controlled by the Arduino Uno. This newly designed level crossing system will be run with the help of sustainable renewable energy which is cost-effective, eco-friendly, and apply under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated results of several renewable energy sources including a hybrid system and optimized the Levelized Cost of Energy (LCOE), and the payback periods.

COMPASS is a mission concept aiming to determine the recent climate history of planet Mars. Aboard this mission, there would be a dual mode radar instrument capable of acting both as a Synthetic Aperture Radar (SAR) and a Subsurface Sounder for mapping shallow ice. The COMPASS Radar Observer for Mars Exploration (CROME) instrument will help advance science and exploration. First, the COMPASS mission is described. Then, the CROME instrument concept and its implementation are presented. Finally, the procedure used to predict the performance is detailed for both the Side-Looking SAR and the Nadir-looking Sounder modes.

Whereas waste management research has focused on mostly urban municipalities in South Africa, not much is known about the current performance of waste management services from the perspective of community residents in rural municipalities. This study reports on the status and effectiveness of waste management practices from the perspective of community residents in the Umkhanyakude and Zululand Districts in the rural areas of the KwaZulu-Natal province of South Africa. Making use of structured questionnaires, primary data was obtained by interviewing 333 respondents representing households in the study area. Results have revealed several shortfalls in the provision of municipal waste management services, except for solid waste removal from households (66%), and management of landfill sites (41%). The degree of dissatisfaction for waste management services rendered was very high (97.3%). Furthermore, the majority (61.4%) of respondents were not willing to participate in waste segregation due to lack of appropriate knowledge and infrastructure. The study also pinpointed some benefits associated with waste minimization, as well as barriers constraining effective waste recycling. Based on these findings, there is a dire need for transforming current waste management practices toward increased recycling rates by creating more environmental awareness and a supporting infrastructure.

The number of citations that a paper has received is the most commonly used indicator to measure the quality of research. Researchers, journals, and universities want to receive more citations for their scholarly publications to increase their h-index, impact factor, and ranking respectively. In this paper, we tried to analyses the effect of the number of available Google Scholar versions of a paper on citations count. We analyzed 10,162 papers which are published in Scopus database in year 2010 by Malaysian top five universities. Then we developed a software to collect the number of citations and versions of each paper from Google Scholar automatically. The result of spearman correlation coefficient revealed that there is positive significant association between the number of Google Scholar versions of a paper and the number of times a paper has been cited.

Rainfall is a major climate parameter whose variation in space and time influences activities in different weather sensitive sectors such as agriculture, transport, and energy among others. Therefore, accurately forecasting rainfall is of paramount importance to the development of these sectors. In this regard, this study sought to contribute to quantitative forecasting of rainfall over Eastern Uganda through assessing the Weather Research and Forecasting model’s ability to simulate the intra–seasonal characteristics of the September to December rain season. These were: onset and cessation dates; wet days and lengths of the wet spells. The data used in the study included daily ground rainfall observations and lateral and boundary conditions data from the National Centers for Environmental Prediction (NCEP) final analysis at 1 0 horizontal resolution and at a temporal resolution of 6 hours for the entire study period were used to initialize the Weather Research and Forecasting (WRF) model. The study considered four weather synoptic weather stations namely; Jinja, Serere, Soroti and Tororo. The results show that the WRF model generally simulated fewer wet days at each station except for Tororo. Also, the WRF model simulated earlier onset and cessation dates of the rainfall season and overestimated the length of the wet spells.

This study was conducted in a laboratory-controlled environment aiming at studying the physical properties and elemental composition of coal combustion particles in a brazier. Particles were sampled ~1 m above the stove using a partector, where particles were collected on gold Transmission Electron Microscopy (TEM) grids and polycarbonate filters for TEM and inductively coupled plasma mass spectrometry (ICP-MS) analysis, respectively. Particles for elemental analysis collected on a 37 µm polycarbonate filters whereby a Gillian pump was used to draw in air. During sampling, a 2.5 µm cyclone was attached to the sampling cassette to isolate larger particles. The results have shown that combustion particles emitted during the early stage of combustion where single spherical particles with a diameter of around 450 nm. As the combustion progresses, the particle diameter gradually decreases and the morphology changes to accretion chain and fluffy bead structure for the flaming and char-burning phase, respectively.

This study aimed to use remote sensing and GIS to detect changes in surface water and wetlands in the Kumasi Metropolis over a period of 20 years (2002-2022). Landsat images were processed using the QGIS software, and the Maximum Likelihood Algorithm was used to perform supervised classification of land use land cover, while raster calculator aided in detecting wetlands by calculation for NDWI, MNDWI, and AWEI. The results showed that the majority of the landscape in the Kumasi metropolis was made up of built-up areas (79.4%), followed by agricultural lands (13.3%) and wetlands (7.3%). Over the 20-year period, built-up areas had gained 20.7% of the total landscape, while agricultural lands and wetlands had lost 16.7% and 4.0%, respectively. All water index methods recorded an increase in non-water cover and a loss of water cover over the period, with surface water decreasing by 5% and non-water land cover increasing by 5%. The study concludes that there have been significant changes in wetlands and surface water land use and land cover within the Kumasi Metropolis over the past 20 years, and recommends the enforcement of legislation on surface water protection in the area to protect wetlands

Over 3.0 billion people of the 7.75 billion globally suffer from malnutrition due to deficiencies in micronutrients, particularly iron, zinc, and vitamins (such as vitamin A, B complex, E, and folate, etc.). In response, there is growing consumer interest in foods that provide traditional nutrients and contain additional compounds beneficial to health and well-being. Wheat is the most important cereal crop in terms of both production and consumption, contributing about 30% of total cereal consumption worldwide. It provides a significant source of carbohydrates and gluten protein (around 12-14% protein), which supply substantial energy. Wheat grain is also rich in various nutrients, such as macro-elements like phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg); microelements like iron (Fe), zinc (Zn), manganese (Mn), and vitamins B complex: thiamin (B1), riboflavin (B2), niacin (B3), pantothenic acid (B5), pyridoxine (B6), biotin (B7), folate (B9), additionally soluble fiber and other trace minerals which are higher than in other major cereals. The inner bran coats, phosphates, and other mineral salts in wheat contribute to its beneficial effects on bowel movements. Despite its nutritional benefits, the quality of wheat's nutrients is a topic of ongoing discussion. Exploring ways to enhance the nutritional quality of wheat could provide valuable insights for scientists working to improve global nutrition status.

Over recent years, the swine industry has witnessed the withdrawal of antibiotics and continuous regulation of zinc and copper oxides in the early-life nutrition of piglets. Due to this development, alternative ingredients or additives from plant sources have been tremendously explored. Therefore, this study's objective was to evaluate the effect of dietary supplementation with tannins on weaned piglets’ growth performance, serum antioxidant capacity, and immune status using a meta-analysis approach. With only the inclusion of non-challenged weaned piglets, a total of 16 studies with parameters of interest were deemed eligible after a two-step screening process following a comprehensive literature search in the scientific databases of Web of Science, Scopus, ScienceDirect, PubMed, and Google Scholar. Two main categories of data, including the study’s characteristics and response variables (growth performance, serum antioxidant, and immune indices), were extracted. Applying the random-effects models, Hedges’ g effect size of supplementation with tannins was calculated using the R software to determine the standardized mean difference (SMD) at a 95% confidence interval. Sub-group analysis and meta-regression further explored heterogeneity (PSMD 0.05, I2 50%, n ≥10). Supplementation with tannin reduced average daily gain (p = 0.20) and feed conversion ratio (p 0.01); however, it increased average daily feed intake (p = 0.07) and final body weight (p 0.01). Meta-regression models indicated that tannin dosage and supplementation duration were directly associated with tannins' effectiveness on productive performance. In the serum, the concentration of glutathione peroxidase, superoxide dismutase, and total antioxidant capacity were elevated (p 0.01) in response to tannins supplementation, while malondialdehydes was reduced (p 0.01). Likewise, decreased immunoglobin A (p = 0.47) and increased immunoglobin M and G levels (p 0.01) were detected. In conclusion, dietary supplementation with tannins, particularly with chestnut and grape seed proanthocyanidins, increases the productivity of weaned piglets. At the same time, it is a possible nutritional strategy to mitigate oxidative stress and stimulate gut health. Thus, supplementing chestnut and grape seed proanthocyanidin tannins in the early phase of swine production could be used to alleviate the incidence of diarrhea.

Background: Medicinal plants are of great importance to health of individual and communities. About 80% of the population in Uganda relies on traditional medicine because western-trained medical personnel are limited especially in villages. Most Ugandans use Hymenoxys odorato for medicinal purposes e.g. to treat colds, fever, coughs, anti-helminthes, locally used as tea, anti-allergy and also as an anti-venom to relieve snake bites. Method: A group of 25 male wistar rats of 150 g–210 g were kept for 14 days while being fed and treated with the extract. At 14^th day, anesthesia was given and blood samples collected by cardiac puncture for hematological and biochemical investigations. Serum was analyzed for Alkaline Phosphatase, Aspartate Transaminase and Alanine Transaminase while whole blood was used for complete blood count. The liver and kidney were removed and placed in 10% formalin to prepare for histology staining using haematoxylin and eosin technique. Results: The extract elevated hepatic biomarker enzymes i.e. ALP, ALT and AST. The increase was found to be significantly different (P > 0.05) at 400 and 500 mg/kg doses as compared to the control group. Histological sections of the liver showed distortion of liver cytoarchitecture, steatosis, necrosis of hepatocytes and congestion of the sinusoids at high doses 300, 400 and 500 mg/kg body weight. In the sections of the kidney, there was mild distortion of the integrity of the kidney with glomerular hypercellularity at high doses (400 and 500 mg/kg per body weight). Conclusion: Hymenoxys odorato aqueous extract has toxic effects on the liver and kidney of wistar rats. The effects were observed to be in a dose dependent manner.

The rapid expansion of transcriptomics from increased affordability of next-generation sequencing (NGS) technologies generates rocketing amounts of gene expression data across biology and medicine, and notably in cancer research. Concomitantly, many bioinformatics tools were developed to streamline gene expression analysis and quantification. We tested the concordance of NGS RNA sequencing (RNA-seq) analysis outcomes between the two predominant programs for reads alignment, HISAT2 and STAR, and the two most popular programs for quantifying gene expression in NGS experiments, edgeR and DESeq2, using RNA-seq data from a series of breast cancer progression specimens, which include histologically confirmed normal, early neoplasia, ductal carcinoma in situ and infiltrating ductal carcinoma samples microdissected from formalin fixed, paraffin embedded (FFPE) breast tissue blocks. We identified significant differences in aligners’ performance: HISAT2 was prone to misalign reads to retrogene genomic loci, STAR generated more precise alignments, especially for early neoplasia samples. edgeR and DESeq2 produced similar lists of differentially expressed genes in stage comparisons, with edgeR producing more conservative, though shorter, lists of genes. Albeit, Gene Ontology (GO) enrichment analysis revealed no skewness in significant GO categories identified among differentially expressed genes by edgeR vs DESeq2. As transcriptome analysis of archived FFPE samples becomes a vanguard of precision medicine, identification and fine-tuning of bioinformatics tools becomes critical for clinical research. Our results indicate that STAR and edgeR are well-suited tools for differential gene expression analysis from FFPE samples.

AVs suffer reduced maneuverability and performance due to the degradation in sensor performances in fog. Such degradation causes significant object detection errors essential for AVs' safety-critical conditions. For instance, YOLOv5 performs significantly well under favorable weather but suffers miss detections and false positives due to atmospheric scattering caused by fog particles. Existing deep object detection techniques often exhibit a high degree of accuracy. The drawback is being sluggish at object detection in fog. Object detection methods with fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persist. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar's object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image on the camera image. Using the attention mechanism, we emphasized and improved important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our result shows that the proposed method significantly enhances the detection of distant and small objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed with an accuracy of 0.849 at 69 fps.

Cancer ranks second among the causes of mortality worldwide,following cardiovascular diseases.Brain cancer, in particular, has lowest survival rate of any kind of cancer. Brain tumours vary in their morphology, texture, and location, which determine their classification. Accurate diagnosis of the tumour category enables physicians to select optimal treatment strategies and potentially prolong patients’ lives. Researchers who also implemented deep learning models for diagnosing diseases in recent years largely focused on deep neural network optimization to enhance neural network performance. This implicates implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. This paper presents a novel hybrid approach method for improved brain tumour classification by combining CNN and EfficientNetV2B3 as feature extraction, followed by (KNN) for classification. To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas,pituitary,and gliomas tumours.Images from two classes of normal brain and brain tumour were included in the second dataset, which was obtained from Kaggle.In order to enhance performance even more, the study concatenates the CNN and EfficientNetV2B3 flattened outputs before feeding them into the KNN classifier. The proposed framework run on two different dataset and demonstrates outstanding performance with an accuracy of 99.51% and 99.8% on each dataset.

This paper investigates the effects of air void topology on hydraulic conductivity in asphalt mixtures with porosity in the range 14%-31%. Virtual asphalt pore networks were generated using the Intersected Stacked Air voids (ISA) method, with its parameters being automatically adjusted by the means of a differential evolution optimisation algorithm, and then 3D printed using transparent resin. Permeability tests were conducted on the resin samples to understand the effects of pore topology on hydraulic conductivity. Moreover, the pore networks generated virtually were compared to real asphalt pore networks captured via X-ray Computed Tomography (CT) scans. The optimised ISA method was able to generate realistic 3D pore networks corresponding to those seen in asphalt mixtures in term of visual, topological, statistical and air void shape properties. It was found that, in the range of porous asphalt materials investigated in this research, the high dispersion in hydraulic conductivity at constant air void content is a function of the average air void diameter. Finally, the relationship between average void diameter and the maximum aggregate size and gradation in porous asphalt materials was investigated.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

This study explores the potential of Eisenia fetida (Savigny) in vermicompost production within vermipits during winter months in the Rampur region of Uttar Pradesh, India. The vermipits were inoculated with 0.33 kg of E. fetida per pit, against the recommended rate of inoculation for assessment of real-time potential of earthworm on seasonal and regional grounds. The results revealed a significant increase in earthworm population, with three times production (4.1 kg) of the inoculated number (1.0 kg). The productivity dynamics of compost vs E. feida revealed a notable threefold and fourfold effectiveness, respectively. This dual advantage signifies the success of composting process, with the increased worm population indicating enhanced decomposition. The study attributes the combination of regional and edaphic factors viz., sandy loam soil with high humus content and good water passage, facilitating earthworm survival, reproduction and decomposition of agricultural waste. The loose and perforated soil structure allowed easy penetration of earthworms, transforming the soil into fertile compost. The research emphasizes the importance of soil moisture of semi-arid ecosystems thus highlighting the seasonal significance governing earthworm activity. The study provides insights into the mechanical ability of earthworms, its sensitivity to soil compaction and the influence of high hydration of the region. Inoculation during the winter period indicates increased earthworm activity setting the stage for enhanced compost production in the upcoming hot periods of summer months. The findings suggest that optimizing vermicomposting practices with altered feeding stocks, could further improvise compost and earthworm production and productivity in subsequent seasons. The study contributes for sustainable utilization of E. fetida in composting, its regional and seasonal efficacy turns trash to treasure for enhanced agricultural efficacy.

Institutional barriers can hinder effective access and utilisation of Newcastle disease vaccines among smallholder chicken farmers. Many studies have focused on formal institutional barriers with minimal focus on informal institutions - unwritten rules and regulations that govern access and utilisation of Newcastle vaccines. However, informal institutions are more profound and encultured in individuals’ daily activities. This study sought to investigate informal institutional barriers to access and utilisation of Newcastle disease vaccines among women smallholder chicken farmers in Makueni, Kenya. The cross-sectional qualitative study employed in-depth interviews, key informant interviews and focus group discussions as data collection methods. Study informants were conveniently and purposively sampled. Informal institutional barriers to access and utilisation included: fear of Newcastle disease vaccine as a new technology, use of herbal remedies, mistrust of community vaccinators, gender division of labour, ownership of household resources and beliefs that indigenous chickens do not need vaccines. The study concludes that women chicken farmers are constrained by unwritten rules, norms, regulations and gender roles that hinder their access to and utilisation of the Newcastle disease vaccines. The need to examine informal institutions to identify and eradicate barriers to access and utilisation of Newcastle disease vaccines by farmers is recommended.

In 2018, Iran witnessed approximately 21,000 fatalities resulting from road accidents, ranking it as the fifth leading cause of death across all age groups. These incidents carry substantial economic and social burden for the nation, claiming more than 5% of its GDP. Human behavior emerges as a significant contributing factor to high-risk driving offenses. It has been proposed that by influencing general deterrence, a rigorous defense can be established against such violations. Various theories posit that by reinforcing or diminishing influential factors, general deterrence perception can be molded, presenting a potential avenue for an effective deterrent against traffic infractions. This study examines deterrence perceptions prevailing among the public in Iran regarding the enforcement of traffic laws. Employing Structural Equation Modeling (SEM), we delve into the intricate relationships between variables, allowing for a comprehensive exploration of the factors influencing general deterrence. The study draws on the data derived from a survey comprising 548 questionnaires for modeling purposes. An innovative concept, private sector enforcement, is introduced and incorporated into the questioning and modeling process. The findings reveal that socioeconomic status (p-value&lt;0.01), police enforcement (p-value&lt;0.05), private sector enforcement (p-value&lt;0.05), specific deterrence (p-value&lt;0.01), and the use of technology (p-value&lt;0.01) have direct and indirect impacts on the general deterrence perception of drivers.

Businesses now formally enter the phase of "think power differentiation and gap" in response to the new economic climate. While AI, big data, empowerment via the internet, and other aspects of the digital economy help to improve technical capabilities and save operating costs, they also push businesses to innovate their strategies at a faster rate. Enterprises are strategically choosing to de-enterprise, moving from focusing on a single business functioning benefit to building an ecosystem of platform services that will give them a competitive edge. Additionally, the organization's structure is evolving continually from a vertical model to a curved working style, in order to achieve this, businesses must recognize the importance of data analysis in day-to-day management and operations, encourage the development of an organizational atmosphere that is driven by data, fully utilize digital technology, modularize areas or production links that perform related tasks, and encourage the reform of organizational structures. By streamlining the output of the product or service to the external community while also enhancing the company's financial performance model, switching from process-to result-oriented evaluation, and putting in place a customized authority and accountability framework. Replace outcome-focused evaluation with process-focused evaluation, and create a customized incentive scheme that balances power, accountability, and rewards.

In dry season paddy farming, the alternate wetting and drying (AWD) irrigation improves water productivity, paddy production, and has the potential to decrease greenhouse gas (GHG) such as methane (CH4) and nitrous oxide (N2O) emissions when compared to continuous flooding (CF). However, there is a lack of research in Bangladesh on the effects of water management on CH4 and N2O emissions. During November 2017–April 2018, participatory on-farm trials were conducted at Feni and Chattogram districts of Bangladesh. Total 105 farmers comprising 20-hectare of land (62 farmers at Feni and 43 farmers at Chattogram district, each location having 10 hectare of land). We compared irrigation water and cost reductions, paddy yield, and CH4 and N2O emissions from paddy fields irrigated using AWD and CF irrigation methods. The CH4 and N2O emissions were determined using the Cool Farm Beta-3 methodology, and the global warming potential (GWP) was estimated using the Intergovernmental Panel on Climate Change-2014 standard approach. The mean results of randomly selected 30 farmers from two locations (15 of each) showed that AWD remarkably decreased irrigation water consumption by about 24% and increased water productivity by 224%. We estimated 23% savings for irrigation costs in AWD. By this time, AWD improved paddy production by 3% over CF. The AWD irrigation resulted in a 47% reduction in cumulative CH4 emissions having a lower CH4 emission factor (0.74 kg ha-1 day-1) than CF (1.39 kg ha-1 day-1). There was no obvious difference in N2O emission between AWD and CF. When compared to CF, AWD decreased the overall GWP by 27% and lowered the GHG intensity by 42%. The CH4 and N2O emissions did not differ substantially between Feni and Chattogram.

Traditional olive orchards have been grown for thousands of years and still occupy most of the world’s olive cultivation area. To compete with olive oil produced in the higher-yielding intensive orchards, the oil from traditional orchards must be of high quality. We evaluated oil quali-ty—potential and actual (under commercial conditions)—and identified the stages in the pro-duction chain that reduce oil quality in the traditional sector in the Middle East region. Our findings show a clear negative impact of growers' traditional practices on both chemical and sensorial characteristics of the olive oil. The oil originating from the commercial process had higher free fatty acid and lower polyphenol and carotenoid contents, lower stability, lower pungency, fruitiness and bitterness, and a higher prevalence of organoleptic defects than oil originating from fruit picked from the same trees in the experimental procedure. The current, commonly harvesting technique of pole beating significantly increased fruit injury and fruit with mold, leading to a reduction in oil polyphenols and an increase in free fatty acid level compared to manual picking. After harvest, storing the fruit for more than 48 h in plastic bags reduced oil quality dramatically. The traditional olive orchard could potentially be a source of high-quality Extra Virgin Olive Oil. However, handling of the fruit—from the trees until the end of the oil-extraction process—is done incorrectly, adversely affecting oil quality.

Several transgenic rice lines have been developed and are currently under field trials around the world. There are future plans for the commercial release of transgenic rice into the environment. Rice is an autogamous plant and therefore not perceived to be a very high candidate for pollen mediated gene flow to wild and weedy relatives. However, in a tropical environment like Ghana, where sexually compatible wild Oryza species which belongs to the AA genome are present within the ecology of cultivated rice, the possibility of gene flow to wild species cannot be overlooked. There is little evidence on gene flow and its consequences on the wild rice species should they acquire useful genes through gene flow. This review discusses the chances of cultivated to wild rice gene flow in Ghana and the biosafety considerations that should be put in place before the commercial release of genetically modified (GM) rice.

There is a case about a system comprising two queues of M/M(a)/1/K with a due date with FCFS service policy in each queue. Using the methods of simulation and mathematical analysis, the solution diagrams were illustrated for the fixed due date Ɵ=1 second at the phase of due date up to the commencement of the service, limited capacity K₁=5 and K₂=4, for both scenarios of different service management rate resulted. For the analytical solution, first, a SAN model from the system was created in the Mobius tool. For simulation, a java discrete systems simulation code was used. Adjustment of the initial values, selection of the scenarios for service rate, the number of the consumers in the simulation, etc. were all carried out in the simulation code.

Ionic liquid MIE-NH2 displays a new role of development of modification of glycoproteins of lactoferrin through a reductive amination mechanism to synthesize versatile pharmaceuticals. This work introduces a new strategy of modification of Lactoferrin by using methylimidazolium N-ethylamine, this ionic liquid MIE-NH2 linked to N-glycans in Lactoferrin derivatives. Using UPLC/ESI-QTOF and MALDI-TOF mass spectrometry to perform and detect the modifying of ionic liquid-linked glycoproteins. Relevantly, modifying the lactoferrin by MIE-H2 as a small molecule of ion liquid lactoferrin (IL-Lf), which could be a potential antiviral drug and it is achieved by inhibiting various targets. The probability of the lactoferrin modified as a small IL-Lf-molecule to inhibit any of these targets was investigated to find out its potency as a SARS-CoV-2 inhibitor. Molecular docking disclosed the activity of modifying glycoproteins - small IL-Lf-molecules containing amino groups and interaction with targeted Mpro, RdRp, TMPRSS2, and PLpro. Clinically, this study shows the a volubility to provide small IL-Lf-molecules as significantly important drugs that target main protease (Mpro), RNA dependent RNA polymerase (RdRp), transmembrane protease serine 2 (TMPRSS2), and Papain-like protease (PLpro).

Water scarcity is a significant challenge facing Jordan today. It is a challenge in all areas that use water, and it has become certain that water is the critical factor in the population/resources equation. The water resources in Jordan have become limited with the noticeable increase in the population, mostly resulting from forced migrations in neighboring countries because of the wars and until our time. The rising natural population growth rate, along with the massive influx of refugees, has turned into a state of disproportion between the daily consumption of the population and the amount of water available. That Jordan shares most of its surface water resources with neighboring countries has exacerbated the situation. The current use of water already exceeds its renewable supply. Excessive withdrawal from aquifers, which leads to a lowering of the water table and deterioration in water quality, covered the deficit. This paper focuses on assessing the water situation in Jordan, mainly evaluating this problem and the solution being considered, the true basis for a sustainable water solution requires awareness by the population, and several governmental and non-governmental organizations are actively involved in educating residents about water shortages. The most important and 'actionable' elements of comprehensive water solutions are discussed in this paper, and these elements exist, develop extra water supplies, water harvesting, water desalination, proper wastewater reuse in the agricultural sector, and reduce the demand for drinking water. This research provides specific recommendations to address the shortage of water resources in the Kingdom and highlights the importance of water conservation and optimal use.

Age-related changes in behavior and sensory perception have been observed in a wide variety of animal species. In ants and other eusocial insects, workers often progress through an ordered sequence of olfactory-driven behavioral tasks. Notably, these behaviors are plastic, and workers adapt and rapidly switch tasks in response to changing environmental conditions. In the Florida carpenter ant, smaller minors typically perform most of the work needed to maintain the colony while the larger majors are specialized for nest defense and rarely engage in these routine tasks. Here, we investigate the effects of age and task group on olfactory responses to a series of odorant blends in minor and major worker castes. Consistent with their respective roles within the colony, we observed significant age-associated shifts in the olfactory responses of minors as they transitioned between behavioral states, whereas the responses of majors remained consistently low regardless of age. Furthermore, we identified a unitary compound, 3-methylindole, which elicited significantly higher responses and behavioral aversion in minor nurses than in similarly aged foragers suggesting that this compound may play an important role in brood care. Taken together, our results suggest that age- and task-associated shifts in olfactory physiology may play a critical role in the social organization of ant colonies.

The immune response is crucial in Pregnancy Loss (RPL), which affects 1-2% of women. In RPL, peripheral blood lymphocytes and local immune cells are activated with a concomitant decrease in the leukocyte tolerogenic response in the uterus and decidua. IL-17 plays an essential role in T cell activation, weakening tolerogenic responses of the T cells. The report aims to analyze the IL-17 genetic polymorphism, circulating IL-17 lymphocyte populations, the reaction upon lymphocyte priming, the amount of cytokine in serum, and the number of T-reg cells in a group of 50 RPL and 50 normal women from the admixed Venezuelan population. No differences were observed in the genetic polymorphisms rs2275913 and rs763780. A significant decrease (P&lt;0.001) was observed in RPL patients compared to controls in the IL-17+ cell populations CD3+, CD8+, CD56+, and CD4CD25 FoxP3, but not in the CD4IL17+ cells. The response upon cell priming was significantly decreased (P&lt;0.001) in all subpopulations of RPL as compared to controls. It is concluded that despite the increase in IL-17A in the serum of patients, the number of circulating IL-17 cells and the lymphocyte response to stimulation was significantly decreased (p&lt;0.001). No differences in the genetic polymorphism compared to controls suggest that biological factors influence IL-17 levels in RPL patients, which must be carefully studied.

In ethnobotany, the availability hypothesis predicts that plants that are abundant and easily accessible to people are more likely to be medicinal than not. By protecting species diversity away from people, protected areas (PAs) may act as a limiting factor to a sustainable development of traditional knowledge concerning medicinal uses, and in so doing, PAs provide opportunity to prioritize ex-situ conservation for species that are PAs restricted. In this scenario, ex-situ conservation becomes the only chance for people to develop traditional knowledge on plants which otherwise wouldn’t be documented as traditionally useful to people. To test these expectations, we used data collected for almost 20 years of fieldworks on plant medicinal uses and their abundance inside and outside the Kruger National Park (KNP), South Africa. We fitted four different scenarios of structural equation models (SEMs) to the data collected. We found that total plant abundance (abundance outside + inside KNP) is a significant positive predictor of medicinal status, and so is abundance outside KNP, thus supporting the availability hypothesis. However, not only abundance inside KNP is not a direct significant correlate of medicinal status, but also the relationship between both is negative. The lack of predictive power of inside-abundance is most likely because some species are exclusively found inside KNP, and local communities do not have access to them. It also shows that the positive and direct correlation of total abundance with medicinal status is driven by outside-abundance. In addition, the negative relationships between inside abundance and medicinal status implies that abundant plants inside KNP tend to be not-medicinal, further providing evidence that PAs hinder the development of medicinal knowledge. Furthermore, when inside and outside abundance were included simultaneously in a model as two distinct variables, inside abundance was never a direct significant predictor of medicinal status, but it was so, via an indirect path mediated by outside abundance. This suggests that outside abundance is the key variable driving the development of medicinal plant knowledge. Cumulatively, our findings suggest that anything that promotes the growth of PA-restricted plants beyond the natural realized niches of these plants (ex-situ conservation) such as in botanical gardens, private gardens, in agroforestry systems, etc., is to be promoted so that people-plant interactions may continue for the benefits of ethnobotanical knowledge development.

This study introduces a novel nanocomposite coating composed of PANI/CeO2 nanocomposite films, aimed at addressing corrosion protection needs. Analysis through FTIR spectra and XRD patterns confirms the successful formation of the nanocomposite films. Notably, the PANI/CeO2 nanocomposite films exhibit a hydrophilic nature. The bandgap energy of the PANI composite film measures at 3.74 eV, while the introduction of CeO2 NPs into the PANI matrix reduces the bandgap energy to 3.67 eV. Furthermore, the electrical conductivity of the PANI composite film is observed to be 0.40 S.cm-1, with the incorporation of CeO2 NPs leading to an increase in electrical conductivity to 1.07 S.cm-1. To evaluate its efficacy, electrochemical measurements were conducted to assess the corrosion protection performance. Results indicate a high protection efficiency of 92.25% for the PANI/CeO2 nanocomposite film.

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. We also ask the question, "Do different input classes have a relationship or are they unrelated?" For instance, could there be an overlapping set of highly active neurons to identify different classes? Could there be a set of neurons that are useful for one input class whereas misleading for a different one? Intuition answers these questions positively, implying the existence of a structured set of neurons inclined to a particular class. Knowing this structure has significant values; it provides a principled way for identifying redundancies across the classes. Here the interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. We also propose an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image. Our relevance score verifies the interpretability scores in terms of neurons tuned to a particular concept/class. Further, we perform network ablation and measure the performance of the network based on our approach.

To harness light-matter interaction at nano-/micro-scale better control tools have to be developed. Here, it is shown that by applying external electric and/or magnetic field, ablation of Si and glass under ultra-short (sub-1~ps) laser pulse irradiation can be controlled via the Lorentz force $\mathbf{F} = e\mathbf{E} + e[\mathbf{v}\times\mathbf{B}]$, where $\mathbf{v}$ is velocity of charge $e$, $\mathbf{E}$ is the applied electrical bias and $\mathbf{B}$ is the magnetic flux density. The external electric E-field was applied during laser ablation using suspended micro-electrodes above glass substrate with an air gap for the incident laser beam. The counter-facing Al-electrodes on Si surface were used to study debris formation patterns on Si. Debris was deposited preferentially towards the negative electrode in the case of glass and Si ablation. Also, an external magnetic field was applied during laser ablation of Si in different geometries and is shown to affect ripple formation. The vectorial nature of the Lorentz force widens application potential of surface modifications and debris formation in external E-/B-fields with potential applications in mass and charge spectroscopes.

Fourteen (14) rack-mounted polycrystalline modules installed on the concrete roof of the solar energy applications laboratory at the Kwame Nkrumah University of Science and Technology (KNUST) in Ghana, a hot humid environment were assessed after 19 years of continuous outdoor expodure. The physical state of the modules was documented using a visual inspection checklist. They were further assessed by current-voltage (I-V) characterization and thermal imaging. The modules were found to be in good physical state, except some bubbles on front side and minor discolouration/corrosion at edge of the cells. Compared with reference values, the performance decline of the modules observed over the exposure period was: nominal power (P_nom), 21% to 35%; short circuit current (I_sc), 5.8% to 11.7%; open circuit voltage (V_oc) 3.6% to 5.6% and 11.9% to 25.7% for fill factor (FF). It is hoped that this study will provide some helpful information to project developers, manufacturers and the research community on the long-term performance of PV modules in Ghana.

According to many researchers, Abstraction is the basis of mathematics, computing, counting devices, and computer science and engineering. What is more, all of the above deal with complexity management in some way, and abstraction is the most basic mechanism of complexity management.Generative software development - whether in the sense of empowering humans by machine to create software or in the sense of reusing products - has been and is one of the serious concerns and goals of software engineering. The interesting thing is that in both views of generativity, the main issue is still, in a way, complexity management: whether this complexity management is to achieve diversity and reuse management (Czarnecki’s approach) or to Structuring from existing structures (the approach of Alexander and his followers in an object-oriented society).In this article, we will first look at complexity and its various definitions. The definitions that show, despite the different perspectives on complexity in different disciplines and domains, all point in one direction. We will conclude that complexity is rooted in multiplicity. In the following, we will formally define complexity. In the following discussion, we will look at the generative patterns of software development, and then we will look at the complexity management patterns at seven levels.In this article, the author has tried to maintain a comprehensive approach to complexity and to consider the approaches of different domains to complexity.

The novel Ionized Gas Thermoelectric Generator (IG-TEG) system that has been studied theoretically showing capabilities to continually extracting energy from the thermal energy of the ambient air, at low temperatures within the standard room temperature and below. This system does not need a temperature gradient in order to work, unlike the other TEGs that use Seebeck effect, and therefore this new system can be utilized for cooling purposes, by extracting energy instead of wasting energy in compressing the gas for cooling. This novel system was designed based on Static Ratchet Potential (SRP), which is known as a spatially asymmetric electric potential produced by an array of positive and negative electrodes. The ratchet potential produces electrical current from random Brownian Motion of charged particles that are driven by thermal energy. Ratchet potential was studied and investigated by several researches in the fields of sensing and energy harvesting. The main ratchet potential system parameter is the particles transportation, this parameter was studied under the condition of flashing ratchet potentials, and was analyzed based on several methods. In this study, a different approach is pursued to estimate particles transportation, by evaluating the charged particles distribution, and applying the other conditions of the SRP.

Forensic hospitals throughout the country house individuals with severe mental illness and history of criminal violations. Insomnia affects 67.4% of hospitalized patients with chronic neuropsychiatric disorders, indicating that these conditions may hijack human somnogenic pathways. Conversely, somnolence is a common adverse effect of many antipsychotic drugs, further highlighting a common etiopathogenesis. The role of dysfunctional mitochondria in psychopathology is well-established, however, the association of these organelles with sleep physiology is novel. Indeed, reducing neuronal oxidative stress by importing mitochondria from astrocytes, may be the purpose of human slumber. This model may explain mitochondrial dysfunction during anesthesia as well as in the rare genetic disease, fatal familial insomnia. In this narrative review, we focus on the salience network of the brain, a common denominator for insomnia, neuropsychiatric and neurodegenerative disorders. We also discuss mitochondria-protecting strategies, including membrane lipid replacement, natural and synthetic phenazine and phenothiazine derivatives.

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

This paper discusses the use of a participatory approach to assess the level of understanding of the Water-Energy-Climate Change nexus in South Africa. The aim is to facilitate the development of well-coordinated, systematic, and holistic strategies for efficient management of the nexus and its implications in the country. The assessment was guided by the broader Integrated Water Resource Management framework, which promotes a participatory approach in the administration of water resources. The paper argues that despite the reasonable level of understanding of WECC, there is still a lack of integrated policy development and planning among key stakeholders. This is exacerbated by limited coordination and consultation among these stakeholders, particularly policymakers. This necessitates the urgent adoption of holistic and systems thinking approaches, and the promotion of collaboration among different stakeholders mandated to manage WECC sectors. Until such approaches are adopted, the WECC nexus will continue to impede the country’s socio-economic development and environmental wellbeing.

In recent years, wireless networks have undoubtedly become a convenient way to connect to the Internet and provide connection to everyone in any corner of the world. In fact, in this era, people are connected to the internet almost everyday and wireless networks give us this privilege in a seamless manner. A wireless network normally consists of access points and nodes where the access points are responsible to amplify the wireless signals, while the nodes are the gadgets that are receiving these signals. However, with such great convenience provided, many challenges are also faced by the users and stakeholders. With no physical connection to devices, wireless networks are evidently more vulnerable to invisible cyber attacks. In this research paper, it the security issues that cause issues in the wireless networks are discussed. Furthermore, an analytical review of privacy challenges found in these networks is performed; these challenges are segregated into security issues and privacy issues. The paper will then present the methods used in conducting a survey and gathering the research results along with further discussion on the results obtained through this study. Finally, a suitable solution is proposed to prevent and overcome the intrusions faced in terms of security and privacy in wireless network scenarios through detection and response mechanism for mitigation of the problems.

In this paper, we compile the fractional power series method and the Laplace transform to design a new algorithm for solving the fractional Volterra integro-differential equation. For that, we assume the Laplace power series (LPS) solution in terms of power q=1m,m∈Z+, where the fractional derivative of order α=qγ for which γ∈Z+. This assumption will help us to write the integral, the kernel, and the nonhomogeneous terms as a LPS with the same power. The recurrence relations for finding the series coefficients can be constructed using this form. To demonstrate the algorithm's accuracy, the residual error is defined and calculated for several values of the fractional derivative. Two strongly nonlinear examples are discussed to provide the efficiency of the algorithm. The algorithm gains powerful results for this kind of problem. Under Caputo meaning the obtained results are illustrated numerically, and graphically. Geometrically, the behavior of the solution declares that the changing of the fractional derivative parameter values' in their domain alters the style of the attained solution in a symmetrical meaning and fully coinciding to the ordinary derivative value'. From these simulations, the results report that the recommended novel algorithm is a straightforward, accurate, and superb tool to generate analytic-approximate solutions for Integral, and integro-differental equations of fractional order.

This research project focuses on the optimization of the hybrid energy system together with the assistance of thin-film coatings aiming to achieve self-sustainable food and crop storage facilities which will run effectively with its own generated energy. An infrastructure will be designed and constructed that will comprise a hybrid power generation system accompanied by thin-film coated semitransparent and non-transparent construction materials for energy saving. Thin-film low emissivity (Low-E) type coatings will assist the transparent or semitransparent construction materials to reflect most of the infrared (IR-mostly heat) and UV spectra of sunlight without interrupting the visible spectrum and will lead to saving energy consumption by reducing the heat and lighting during day time

Chikungunya is a mosquito-borne viral disease caused by Chikungunya virus (CHIKV. We conducted this study determine the seroprevalence and clinical presentation of Chikungunya infection among outpatients seeking healthcare in Mzuzu City, Malawi. Blood samples were collected from malaria negative and non-septic febrile outpatients with fevers ≥38 °C, for not more than 5 days. The enzyme- linked immunosorbent assay (ELISA) test was used to detect anti-CHIKV IgM antibodies and its results were used to determine seroprevalence of Chikungunya. A total of 119 serum samples were tested, of these, 73 (61.3%) tested positive for anti-CHIKV IgM antibodies by ELISA. Laboratory requisition forms were used to capture demographic information such as age, sex, clinical signs and symptoms presented by the enrolled patients. Age groups of 1-9, 10- 19, 20- 29, 30- 39, 40- 49, and ≥50 years had 17.8% (n= 13), 12.3 %,( n=9), 15.1%) (n=11), 19.2%; (n=14), 17.8% (n=13) and 17.8% (n=13) proportion of seroprevalence respectively. Most of the CHIKV infected individuals presented with fever (52.05%), joint pain (45.21%) and abdominal pain (42.67%). The presence of anti- CHIKV IgM antibodies suggest the presence of recent CHIKV infection and therefore accurate laboratory assays are highly recommended for CHIKV diagnosis and appropriate management of febrile patients.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

One of the major concerns in structural health management (SHM) is the early detection of a growing crack. Using this, future damage due to crack propagation can be mitigated or eliminated by implementing proper repair and maintenance. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks. The research presented in this paper focuses on SHM using AE. A novel AE signal analysis approach was proposed in order to detect crack initiation and assess small crack lengths. Experimental investigation indicated that initiation of a crack could be identified through the statistical analysis of the resulting features of the AE signals. A probabilistic AE-based model for small fatigue crack sizing was developed and the uncertainties of the model were estimated. In addition, a probabilistic model validation approach was implemented to confirm accurate estimation of the results. The outcome of this research can be used to evaluate the integrity of structures under fatigue loading. The proposed approach can also be applied as an approach to manage health and assess prognosis of structures.

Nowadays, one of the biggest concern of human being is greenhouse gas emission, especially carbon dioxide emission in developed and under-developing countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO₂ emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay. The studied region is modelled based on the available input data in terms of Million tons including oil (Million tons), gas (Million tons oil equivalent), coal (Million tons oil equivalent), $R_{ew}$ (Million tons oil equivalent), and Gross Domestic Product (GDP) in terms of billion US dollars.} Moreover, the available patents in the fields of climate change mitigation in six Latin American countries namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay has been reviewed and analyzed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies which account for 60% and 72% respectively.

Background: We aimed to evaluate whether baseline GLS and NT-proBNP and their changes after cardiac resynchronization therapy (CRT) can predict the long-term clinical outcomes and the echocardiographic-based response to CRT (defined by 15% relative reduction in left ventricular end-systolic volume). Methods: We enrolled 143 patients with stable, ischemic heart failure (HF) undergoing CRT-D implantation. NT-proBNP and echocardiogram were obtained before and 6 months after. The patients were followed-up (median: 58 months) for HF-related deaths and/or HF hospitalizations (primary endpoint) or HF-related deaths (secondary endpoint) Results: 84 patients achieved the primary and 53 the secondary endpoint, while 104 patients were considered as CRT-responders and 39 as non-responders. At baseline, event-free patients had higher absolute GLS values (p&lt;0.001) and lower NT-proBNP serum levels (p&lt;0001) than those achieving primary endpoint. A similar pattern was observed in favor of CRT-responders vs non-responders. In Cox regression analysis, lower baseline absolute GLS value (HR=0.77;95%CI, 0.51-1.91; p=0.002), higher baseline NT-proBNP levels (HR=1.55;95%CI, 1.43-2.01;p=0.002) and diabetes (HR=1.27;95%CI, 1.12-1.98;p=0.003) were associated with lower incidence of primary endpoint. Conclusions: In HF patients undergoing CRT-D, baseline GLS and NT-proBNP concentrations may serve as prognostic factors, while they may predict the echocardiographic-based response to CRT.

Tree decline resulting from climate change results in physiological weaknesses, attack by harmful pests and pathogen, and threatens forest ecosystem stability. In the work described here, the effects of drought on wood density, tree ring width, and variations in vessel morphology were investigated in Persian oak (Quercus brantii) in the forests of the Zagros Mountains, Ilam province, western Iran. Radial discs were cut from trunks of declined and healthy trees and wood blocks cut radially from the sapwood, heartwood, and juvenile core. Observations were made on transverse sections from the blocks using microscopy. In trees with decline symptoms, wood density was greater than the healthy trees. Furthermore, declining trees had the narrowest ring width, reduced vessel diameter, vessel area, and highest number of vessels and tylose in pith towards bark. It was concluded that changes in anatomical features are associated with the weakening of the trees and are components in declining tree health.

This study investigates how students' well-being relates to sustainable development and education for sustainability at Prince Mohammad Bin Fahd University, Saudi Arabia. The authors endeavor to answer the question "How Does Students' Well-Being Relate to Sustainable Development Via Education for Sustainable Development?" and four sub-questions. Primary data were collected by semi-structured interviews exploring the viewpoints of twenty female and male students (n=26) aged between 18 and 26. The content analysis results have identified additional well-being and education for sustainability factors that influence sustainable education and, therefore, sustainable development. These antecedents include workload, transparency, grading criteria, fairness, belonging, work-life balance, mental health support, missing emotional intelligence factors, factors related to Education for Sustainable Development, such as instructors' role models, institutional commitment, leadership, and collaboration with government. In addition, the study developed a theoretical model for the relationship between antecedents for Education for Sustainable Development, Education for Sustainable Development, and Sustainable Development in the Saudi context. Finally, the study answers four research questions: What factors contribute to education for sustainable development? What are the antecedents of education for sustainable development? How do university educators contribute to sustainable education? How do the antecedents of education for sustainable development, education for sustainable development, and sustainable development relate?

Limited understanding exists concerning leptospirosis in Zanzibar. The objective of this study is to evaluate the degree of knowledge and awareness of leptospirosis within the urban and peri-urban populations of Unguja. A cross-sectional study was conducted utilizing semi-structured questionnaires from January to April 2022. Two hundred respondents were randomly selected (130 males and 70 females) aged between 18 and 89 years. Descriptive analysis was employed to assess the main trends in knowledge and awareness, and χ2 analysis was utilized to determine associations between demographic characteristics with respondents’ knowledge and awareness. The majority of respondents (64%) lacked awareness of leptospirosis' etiology, but a significant proportion of respondents had a favorable attitude (68.6%) towards leptospirosis compared to their average knowledge and awareness (35%) and practices (29.3%). Nonetheless, the livestock keeper, farmers, fishermen, and healthcare providers had low levels of knowledge and awareness. The findings also demonstrated that males had a strong association with occupational physical activities, while educational level was associated with preventive practices. Living in urban or peri-urban areas was significantly linked with the respondents' practices. The study's outcomes demonstrated low levels of community knowledge and awareness regarding leptospirosis' etiology, mode of transmission, and symptoms among livestock keepers, farmers, fishermen, and healthcare providers. Although most respondents had a favorable attitude, their low level of knowledge and poor practices indicate that supplementing a positive attitude with enhanced knowledge and awareness is necessary to promote individual engagement in preventive measures.

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To automatic parking, controlling steer angle, gas hatch, and brakes need to be learned. Due to the increase in the number of cars and road traffic, car parking space has decreased. Its main reason is information error. Because the driver does not receive the necessary information or receives it too late, he cannot take appropriate action against it. This paper uses two phases: the first phase, for goal coordination, was used genetic algorithms and the Cuckoo search algorithm was used to increase driver information from the surroundings. Using the Cuckoo search algorithm and considering the limitations, it increases the driver’s level of information from the environment. Also, by exchanging information through the application, it enables the information to reach the driver much more quickly and the driver reacts appropriately at the right time. The suggested protocol is called the MODM-based solution. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the parking system performance metrics are enhanced based on the detection rate, false-negative rate, and false-positive rate.

Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

An efficient design of conservation tillage trencher (CTT) saves time, natural resources, and conserves soil health by avoiding vigorous soil disturbance of tillage operations. A two-wheel tractor operated CTT has constructed with modified rotary shafts, toolbar frames, depth control bars, bed shapers and chisel ploughs for conservation tillage operation of sugarcane plantation. The performance evaluation of developed CTT was tested for comparing the suitability with small-scale farmer’s practice (SFP) and large farm practice (LFP). Finally, the CTT is applied for preparing sugarcane farmland in different conservation tillage methods of i) bed cum trenching (BT), ii) zero-tillage trenching (ZT) and iii) strip-tillage (ST). The performance of effective field capacities of CTT were 0.14 ha h-1, 0.12 ha h-1 and 0.14 ha h-1 for BT, ZT and ST, respectively. Developed CTT saved time 80% and 26-37% over SFP and LFP, and required 96% and 26-36% less labour than SFP and LFP, respectively. Conservation tillage required 7-10 l ha-1diesel whereas SFP and LFP required 25 l ha-1and 65 l ha-1, which reduced CO2 emission by 60-72% and 85-89% compared to SFP and LFP, respectively. Ultimately, newly developed CTT saved sugarcane land preparation costs by 86% to 90% compared to conventional tillage systems.

In this COVID-19 pandemic, the use and dependency on Internet has grown exponentially. The number of people doing online activities such as e-learning, remote working, online shopping and others have increased. This has also led to increased vulnerability to cyber crimes. Cyber security attacks have become a serious problem. The common types of cyber security attacks are phishing, malware, ransomware, social engineering, identity theft and denial-of-service. The attackers target the victims in order to get their credential information or financial benefits. Those people who are doing online activities are vulnerable to cyber threats. This is because the network is not safe. The attackers are able to code according to the weaknesses of the Internet. Once the attackers hack into the devices, they have the root access and can do whatever they want to do with the device. In this research paper, the concept of cyber security attack and detailed research about real attacks are discussed. This is followed by detailed review about the recent cyber security attacks with a critical analysis. Moreover, the research paper will be proposing the latest research contribution of cyber security during COVID-19 and the implementation scenario which will give the examples about how the companies maintain privacy as well as the limitations. Then, the paper will be discussing the reasons that people are vulnerable to cyber security and the unique solution to the problems stated. Finally, this paper will conclude with an in-depth analysis and future direction for cyber security research.

This article presents the suitability analysis of onshore wind farm sites using an AHP (Hierarchical Analysis Process) multi-criteria decision-making (MCDM) approach based on geographic information system modeling (GIS). This analysis is based on different technical aspects (wind speed, elevation, slope), economic (proximity to the electricity network, proximity to roads) and social (proximity to residential areas, population density). The introduction of the factor “population density” and the constraint “required area” rarely used in previous studies makes it possible to refine the results. The data is classified into several categories in a gradual manner, namely unsuitable (0); less suitable (1); suitable (2); highly suitable (3) and most suitable (4). The results of the study show us that the largest percentage relating to spatial coverage is observed in the “suitable” range with 50.89% and the corresponding TWPP (theoretical wind power potential) is 1377.926 GW; it is followed by “highly suitable” 5.67% and a TWPP of 153.652 GW; then “less suitable” with 1.04% and a TWPP of 28.163GW. No areas were identified in “most suitable” and finally the “unsuitable” range with 42.4%. The results show that the most favorable areas are located in the part of the far north of the country corresponding to the strong wind zone. The sensitivity analysis was carried out based on scenario tests (technical, economic and equal-weight) in order to give greater visibility of choice to the different stakeholders who would like to invest in onshore wind energy in Cameroon. This study is useful in more than one way because the results obtained can help investors, the government and other stakeholders to identify potential areas for the deployment of wind energy systems in Cameroon. Furthermore, a study of hybrid solar-wind systems could improve the efficiency of electricity parks in Cameroon.

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM-IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Run Final (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001-2020. The Asfezari gridded precipitation data which are developed using a dense of ground-based observation were utilized as the reference dataset. The findings indicate that the IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All the three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggest that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

This study reports a computational investigation on the antioxidant activity of five plant food benzoic acid derivatives, namely gallic acid (GA), para-hydroxybenzoic acid (PHBA), protocatechuic acid (PCA), syringic acid (SA), and vanillic acid (VA). Based on computed thermodynamics parameters, a detailed comparative debate is developed concerning their free radical scavenging activity in the gas phase and polar solutions (in water and methanol solvents). This discussion goes on to elucidate both the most preferred mechanism and the order of antioxidant activity for these molecules in each environment. Paradoxically, calculations using the harmonic oscillator model of aromaticity (HOMA) suggest that H abstraction radicals gain in stability as the central benzene ring loses in structural aromaticity. Finally, spin densities and fukui function f⁰ seem to be good indicators of the local reactivity of these compounds towards free radicals.

Ghana spends over $4000 (US dollars) annually managing municipal solid waste (MSW) with little improvement in regional sanitation. Energy demand in the country is also on the rise. Therefore, addressing waste problems via the waste-to-energy valorization approach is necessary for dual benefits. This paper thus seeks to comprehensively review Ghana's MSW's proximate and ultimate compositions for biological and thermochemical conversions. Studies between 2011 and 2024 were analyzed. The findings revealed that the organic fraction of Ghana’s MSW is currently about 10.47Kt per day. The proximate analysis reveals a moisture content from 13.55 – 61.9 %, volatile matter ranges from 6.24 - 80.25%, and the calorific value ranges from 14.96-21.46MJ/Kg. The ultimate analysis revealed that the carbon-to-nitrogen ratios range from 24.3-32.2, and the hydrogen content ranges from 5.53-22.8%. The sulphur and nitrogen contents were below 0.6 and 1.7% respectively. Approximately 2,534.65 GWh of electricity is estimated from the organic fraction. Ghana’s MSW is more suited for biochemical conversions.

The adverse relationship between viral hepatitis and pregnancy in developing countries was seen as a reflection of retrospective biased hospital-based data collection by the West. However, the discovery of HEV from an epidemic of non-A, non-B hepatitis in Kashmir and documenting increased incidence and severity of hepatitis E in pregnancy from a house-to-house survey unmasked the unholy alliance. Among the family of HEV’s, genotype (gt)-1, with a unique ORF4-encoded protein enhancing viral polymerase activity and viral replication, is the sole HEV that shows this adverse relationship. The epidemics caused by HEV-gt1 and not by HEV-gt2 show adverse relationship with pregnancy. The pathogenesis of the unholy alliance is complex and at present not well understood. Possibly multiple factors play a role in causing severe liver disease in the mother including: infection, replication and damage to the maternal-foetal interface by HEV-gt1; vertical transmission of HEV to foetus causing severe foetal/neonatal hepatitis; and combined viral and hormone related immune dysfunction of diverse nature in the mother promoting viral replication. Management is multidisciplinary and needs a close watch for the development and management of ALF. Preliminary data suggest beneficial maternal outcomes by early termination of pregnancy in patients with lower grades of encephalopathy.

This study investigates the characteristics of formaldehyde (HCHO) concentration and its reduction in newly constructed apartment buildings in Dubai. It addresses the significant health risks of high HCHO levels due to poor ventilation and extensive use of VOC-emitting materials. The research explores the effectiveness of various mitigation strategies, including air purifiers, photocatalyst applications, and window ventilation, in the living room, master bedroom (MBR), Room 1, and Room 2. HCHO concentrations were measured under different conditions: sealed windows, operation of an air purifier with a VOCs filter, and the application of a photocatalyst. The study followed a quantitative approach, recording real-time HCHO levels over 24 hours. Results indicated that sealing windows led to HCHO concentrations surpassing the domestic standard of 0.1 ppm, with the MBR recording the highest concentration at 0.73 ppm. The air purifier showed minimal impact within the first 8 hours but achieved an 18% reduction after 24 hours of operation. Applying a photocatalyst to room surfaces resulted in a 50% reduction in HCHO levels. Ventilation through window openings significantly improved air exchange rates, with the air exchange per hour (ACH) increasing proportionally with window opening size. Smaller rooms with larger window-to-volume ratios, such as Room 1 and Room 2, maintained HCHO concentrations below the WHO standard of 0.1 ppm with partial window openings. The study concludes that window ventilation is the most effective strategy for reducing HCHO concentrations, highlighting the importance of integrating comprehensive ventilation designs in modern residential buildings to ensure healthier indoor environments in Dubai.

In this work, some numerical radius inequalities based on the recent Dragomir extension of Furuta's inequality are obtained. Some particular cases are also provided.

Tea and coffee are the most commonly used beverages throughout the world. Both decoctions are rich in small organic molecules such as phenolics/polyphenolics, purine alkaloids, many methylxanthines, substituted benzoic and cinnamic acids. Many of these molecules are physiologically chemopreventive and chemoprotective agents against many severe conditions such as cancer, Alzheimer, Parkinsonism, inflammation, sleep apnea, cardiovascular disorders, bradycardia, fatigue, muscular relaxation, and oxidative stress. Caffeine, a purine alkaloid, is a common metabolite of both tea and coffee aqueous decoctions and its concentration in tea/coffee depends on the fermentation process, preparation of the water extract and quality of tea leaves/coffee beans. A 250 ml of a coffee cup contains 100-150 mg caffeine while the same volume of strong tea contains 25-40 mg caffeine. The present paper presents the potential of caffeine as a potent chemopreventive agent that can be used for numerous physiological disorders.

Coarse-grained molecular dynamics simulations are employed to investigate the spatiotemporal evolution of vesicles (polymersomes) by the self-assembly of randomly distributed amphiphilic BAB triblock copolymers with hydrophilic A and hydrophobic B blocks in an aqueous solution. The vesiculation pathway consists of several intermediate structures, such as an interconnected network of copolymer aggregates, a cage of cylindrical micelles and a lamellar cage. The cage-to-vesicle transition occurs at a constant aggregation number and practically eliminates the hydrophobic interfacial area between the B block and solvent. Molecular reorganization under-lying the sequence of morphology transitions from a cage-like aggregate to a vesicle is nearly isentropic. The end-to-end distances of isolated copolymer chains in solution and those within a vesicular assembly follow log normal probability distributions. This is attributed to the pre-ponderance of folded chain configurations in which the two hydrophobic end groups of a given chain stay close to each other. However, the probability distribution of end-to-end distances is broader for chains within the vesicle as compared to that of a single chain. This is due to the swelling of the folded configurations within the hydrophobic bilayer. Increasing the hydropho-bicity of the B block reduces the vesiculation time without qualitatively altering the self-assembly pathway.

Carbon nanotubes have sparked a substantial amount of scientific and technological research due to their exceptional mechanical, physical and electrical characteristics compared to conventional materials. As a result, detailed studies on their mechanical properties have been conducted, and static and dynamic behavior of single-walled and multi-walled carbon nanotubes have been proposed using Euler-Bernoulli and Timoshenko beam models. The main objective of this paper is to study the free vibration of a Timoshenko-Ehrenfest single-walled carbon nanotube based on the nonlocal theory and taking surface effects into account. To model these effects on frequency response of nanotubes, we use Eringen’s nonlocal elastic theory and surface elastic theory proposed by Gurtin and Murdoch to modify the governing equation. A modified version of Timoshenko nonlocal elasticity theory - known as the nonlocal truncated Timoshenko beam theory - is put forth to investigate the free vibration behavior of single-walled carbon nanotubes (SWCNT). Using the Hamilton’s principle, the governing equations and the corresponding boundary conditions are derived. Finally, to check the accuracy and validity of the proposed method, some numerical examples are carried out. The impacts of the nonlocal coefficient, surface effects and nanotube length on the free vibration of single-walled carbon nanotube (SWCNT) are evaluated and the results are compared with those found in the literature. The findings indicate that the length of the nanotube, the nonlocal parameter and the surface effect all play important roles and should not be disregarded in the vibrational analysis of nanotubes. Finally, the results show how effective and successful the current formulation is at explaining the behavior of nanobeams.

The eutrophication of open water bodies, especially in coastal water area, has received intensive public focuses in recent years. Biological treatment of organic pollutants in industrial and domestic wastewater requires microbial flora to participate. The environmental impact of these microbes, particularly in regard to the spread of antibiotic resistance genes and pathogenicity in coastal water, remains largely unknown. We initiate studies to examine antibiotic resistance genes (ARGs) in microbial flora and study the mode of antibiotic spread in the coastal waters of Bohai Bay, North China by using high performance liquid chromatography-mass spectrum analysis and quantitative polymerase chain reaction. The metabolic features of bacteria related to the presence of ARGs in the coastal area showed the enrichment of Pseudomonadale species with triple resistance properties (ampicillin, kanamycin and gentamycin) at the estuary site locating adjacent to a coastal sewage treatment plant where both type I and type II polyketide synthase genes and b-lactam type antibiotics are detected. Pseudomonadale order is phylogenetically belonged to Proteobacteria Phylum, Gamma-proteobacteria Class and contains Pseudomonadaceae family and Pseudomonas genus. QPCR analysis suggests that the percent occupation of Pseudomonadale species to total Proteobacteria in the examined stations is close to several sampling stations close to sewage treatment plants, where consistent release of organic wastes occurred.

background: The Islamic Republic of Iran has been under sanctions since 1979. These sanctions were further reinforced between the years 2005 and 2012. This study utilizes available data from Iran post-sanctions to review the impact of economic sanctions on both the mental health and the quality of life of Iranian citizens.Methods: A comprehensive scoping review was conducted to identify studies examining the impact of sanctions on health in Iran. Searches were initiated in 2012 using PubMed/Medline, Embase, Web of Science, Scopus, and the Scientific Information Database (Iranian Database) with keywords such as sanctions, implications, mental health, quality of life, civilian mental health, and Iran. Subsequent to the screening process, secondary reports and letters to the editor were excluded. Only original studies—those collecting data via observation, interviews, surveys, experiments, or literature reviews—were retained for analysis. In addition to these search strategies, reference scanning was undertaken to identify additional articles pertinent to the scope of the current study. This thorough approach facilitated the discovery of relevant and significant articles exploring the effects of sanctions against Iran on the mental health and overall well-being of its civilian population. Following the search, duplicate articles were meticulously identified and removed by the authors. Data were subsequently extracted and summarized, focusing on study characteristics and the observed effects of sanctions on civilian mental health and quality of life in Iran.Result: Out of 165 items identified in the preliminary search, ten articles met the inclusion criteria. These selected articles highlighted the pronounced effects of sanctions on various health-related domains.Conclusion: Since 2012, Iran has been subject to intensified sanctions. There is evidence to suggest that the economic sanctions, imposed by Western nations, and have exerted a negative and detrimental impact on the health of individual Iranians.

In recent years, the rapid expansion of urban areas has led to changes in land use, a reduction in pervious surfaces, and a disturbance in the balance between surface waters and groundwater. These urban changes, combined with inadequate drainage systems and misdirection of runoff, have resulted in severe urban floods, causing numerous fatalities and damage to urban areas. As a result, investigating the effects of flood occurrences and determining proper mitigation measures to reduce flood damage is essential. This paper aims to establish a novel economic-hydrologic model to evaluate the efficacy of Low-impact Development (LID) measures in urban Bronx River catchment, NYC. The model examines reduced peak discharge of flood and imposed damage. Initially, the EPA’s SWMM is used to simulate urban flooding based on recent extreme events. LID scenarios are then implemented, including individual and combined scenarios, to mitigate the adverse impacts of flood occurrences of high to low frequency. By means of the HEC-GeoRAS tool, highly detailed flood inundation maps are generated and seamlessly incorporated into the flood damage estimation model, namely HAZUS, to obtain estimates of the potential damage caused by floods. Using the reduced monetary damages and the implementation cost of LID measures, the benefit-to-cost (BC) ratio is then calculated. Findings indicated that the combined LID scenario is the most effective approach in peak flow reduction. Moreover, Permeable pavement outperforms infiltration trench, bio retention cell, and rain barrel in terms of benefit-to-cost ratio, with rain barrel showing the lowest ratio. The proposed evaluation system for LID measures, with a focus on flood damage reduction as a key benefit, provides valuable insights for identifying optimal scenarios and facilitating informed decision-making.

This survey proposed an evaluation model to analyze and examine different approaches to generativity. In addition to problem domain concepts, the following concepts were used to define this model: Complexity and complexity management, and Systematics view to achieve unified and integrated insight into disparate evaluation criteria. The research's approach to the said concepts is first introduced. Then, the evaluation model is presented.

Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

Although the phenomenon of serial killers has received great attention from media, governments, and public, very little information is known about them and very few theories are presented by researchers specifically their definition and motives for killing. Through cross tabulation analysis of top ninety-eight serial killers, this present study poses six questions that investigate the correlations between, offender's gender, offender's level of education, time span of killing, killing severity, number of victims, killer's type of abuse, motives for killing, and victim's profile. Findings show that males kill more than females and for longer time, less educated serial killers kill more horribly, female serial killers consider their family members easy target, and finally males kill most for enjoyment and sex and females kill for financial gains.

In this work, we introduce the class of h-MN-convex functions by generalizing the concept of MN-convexity and combining it with h-convexity. Namely, let M : [0, 1] → [a, b] be a Mean function given by M (t) = M (t; a, b); where by M (t; a, b) we mean one of the following functions: A_t (a, b) := (1 − t) a + tb, G_t (a, b) = a^1−tb^t and H_t (a, b) := $\frac{ab}{ta+\left(1-t\right)b}=\frac{1}{A_t\left(\frac{1}{a},\frac{1}{b}\right)}$ with the property that M (0; a, b) = a and M (1; a, b) = b. Let I, J be two intervals subset of (0, ∞) such that (0, 1) ⊆ J and [a, b] ⊆ I. Consider a non-negative function h : J → (0, ∞), a function f : I → (0, ∞) is said to be h-MN-convex (concave) if the inequality f (M (t; x, y)) ≤ (≥) N (h(t); f (x), f (y)), holds for all x, y ∈ I and t ∈ [0, 1]. In this way, nine classes of h-MN-convex functions are established, and therefore some analytic properties for each class of functions are explored and investigated. Characterizations of each type are given. Various Jensen’s type inequalities and their converses are proved.

In this investigation magnetic γ-Al2O3 ceramic nanocomposite particles bearing epoxide functionality are prepared following a multistep process. The ultimate nanocomposite particles are named as γ-Al2O3/Fe3O4/SiO2/poly(glycidyl methacrylate (PGMA). The surface property is evaluated by carrying out the adsorption study of Remazol navy (RN), a model reactive azo dye, on both γ-Al2O3/Fe3O4/SiO2 and γ-Al2O3/Fe3O4/SiO2/PGMA nanocomposite particles. The adsorption is carried out at the point of zero charge (PZC) to neutralize the effect of particle surface charge. The adsorption rate is very fast, reached equilibrium (qe) value within five min. Due to mesoporous structure of silica layer γ-Al2O3/Fe3O4/SiO2 nanocomposite particles possessed relatively higher specific surface area and magnitude of adsorption is dependent on the total specific surface area. The introduction of epoxide functionality favored high adsorption capacity in mass per unit surface area. The adsorption process strictly followed Langmuir model. Thermodynamic equilibrium parameters implied that irrespective of surface functionality the adsorption process is spontaneous and exothermic. Pseudo-second-order rate kinetic model is more appropriate to explain the adsorption kinetics.