Sci, Volume 6, Issue 1 (March 2024) – 17 articles

Recent studies have provided evidence, based on analyses of instrumental measurements of the last seven decades, for a unidirectional, potentially causal link between temperature as the cause and carbon dioxide concentration ([CO₂]) as the effect. In the most recent study, this finding was supported by analysing the carbon cycle and showing that the natural [CO₂] changes due to temperature rise are far larger (by a factor > 3) than human emissions, while the latter are no larger than 4% of the total. Here, we provide additional support for these findings by examining the signatures of the stable carbon isotopes, 12 and 13. Examining isotopic data in four important observation sites, we show that the standard metric δ¹³C is consistent with an input isotopic signature that is stable over the entire period of observations (>40 years), i.e., not affected by increases in human CO₂ emissions. In addition, proxy data covering the period after 1500 AD also show stable behaviour. These findings confirm the major role of the biosphere in the carbon cycle and a non-discernible signature of humans. Full article

Many studies have investigated the threat of climate change on wild plants, but few have investigated the genetic responses of crop wild relative populations under threat. We characterized the genetic responses of 10 wild barley (Hordeum spontaneum K. Koch) populations in Israel, sampling them in 1980 and again in 2008, through exome capture and RNA-Seq analyses. Sequencing 48 wild barley samples of these populations representing two collection years generated six million SNPs, and SNP annotations identified 12,926 and 13,361 deleterious SNPs for 1980 and 2008 samples, respectively. The assayed wild barley samples displayed intensified selective sweeps and elevated deleterious mutations across seven chromosomes in response to 28 years of global warming. On average, the 2008 samples had lower individual and population mutational burdens, but the population adaptation potential was estimated to be lower in samples from 2008 than in 1980. These findings highlight the genetic risks of losing wild barley under global warming and support the need to conserve crop wild relatives. Full article

The electrochemical oxidation of levamisole, a glassy carbon electrode, was investigated over the pH range 2.0–10.0. Cyclic voltammetric investigations showed a single oxidation process was recorded, with a peak potential (E_p) shown to be pH-dependent in the range 5.0–8.0; between pH 2.0 and pH 5.0, and above pH 8.0, the E_p was found to be independent of pH, indicating apparent pKa values of 5.0 and 8.0. Peak currents were found to increase with increasing pH values. This voltammetric oxidation process was found to be consistent with a two-electron, two-proton oxidation to the corresponding sulfoxide. Based on these findings, the development of a of method based on the high-performance liquid chromatography separation of levamisole, with electrochemical detection being used for its determination, was explored. The chromatographic conditions required for the separation of levamisole were first investigated and optimized using UV detection. The conditions were identified as a 150 mm × 4.6 mm, 5 µm C₁₈ column with a mobile phase consisting of 50% methanol, and 50%, 50 mM, pH 8.0 phosphate buffer. The technique of hydrodynamic voltammetry was applied to optimize the applied potential required for the determination of levamisole, identified as +2.3 V versus a stainless-steel pseudo-reference counter-electrode. Under the optimized conditions, levamisole exhibited a linear response of 1.00–20 mg/L (R² = 0.999), with a detection limit of 0.27 mg/L. The possibility of determining levamisole in artificial urine was shown to be possible via simple dilution in the mobile phase. Mean recoveries of 99.7%, and 94.6%, with associated coefficients of variation of 8.2% and 10.2%, respectively, were obtained for 1.25 µg/mL (n = 5) and 2.50 µg/mL (n = 5). Full article

The African Growth Opportunity Act (AGOA) has been a crucial trade and development initiative, offering preferential access to qualified Sub-Saharan African (SSA) countries to the United States market since its enactment in 2000. This paper presents a comprehensive review of scholarly articles and policy reports that analyze the impact of AGOA on the economic performance of SSA countries. Employing various econometric methods and data analysis techniques, researchers have investigated the effects of AGOA on trade flows, foreign direct investment (FDI) inflows, employment, economic growth, and poverty levels. The findings reveal that AGOA has positively affected the region’s trade, particularly in apparel, textiles, and agriculture. However, its influence on promoting export diversification and attracting FDI is nuanced, with substantial heterogeneity among the beneficiary countries and industries within each country. While some SSA countries have experienced substantial export growth and FDI inflows, others have not fully leveraged the benefits of AGOA due to absorptive capacity constraints and governance challenges. AGOA’s effectiveness in promoting broad-based employment, GDP growth, and poverty reduction remains an active area of inquiry, necessitating further research to understand the policy’s sustained impact and inform future trade policy designs for SSA countries. Full article

Integrating waste and industrial by-products into concrete is an alternative way to reduce global cement consumption, enhancing its eco-friendliness. In this context, residues with fly ash have been increasingly utilised. Considering the vulnerability of concrete with fly ash to carbonation and, at the same time, its high resistance to chlorides, it is important to investigate the behaviour of these concretes under their combined actions. For this purpose, an experimental investigation was conducted, studying mortar and concrete specimens with 40% replacement of cement with fly ash. These specimens were subjected to a combination of actions (Cl⁻ and CO₂) in two phases: initially through immersion and drying tests, and subsequently through a combination of accelerated tests. Concerning the chloride impact study, free and total chloride profiles were studied. Concerning the impact of carbonation, colourimetric and chemical tests were used. The results demonstrate a significant influence of combined action not only on chloride penetration in cement-based materials with fly ash but also on the development of a carbonation front. Exposure of cement-based materials with fly ash to environments with high Cl⁻ and CO₂ content sequentially may lead, on the one hand, to an increase in carbonation resistance. However, on the other hand, it may result in a substantial reduction in chloride penetration resistance. Full article

Within the expansive domain of maritime safety, optimizing evacuation procedures stands as a critical endeavour. After all, evacuation is literally the last and fundamental safety level afforded to mariners and passengers. Recent incidents have rekindled interest in assessing the performance of this ultimate safety barrier. However, addressing evacuability requires a holistic approach. The authors present herein the setup, simulation, and ultimately evaluation of a novel approach and its ability to rigorously assess multiple innovative risk-control options in a challenging, realistic setting. Moreover, its benchmarking against conventional regulation-dictated evacuation processes is captured distinctively along with the relative effectiveness of each proposed measure. Such measures include smart technologies and procedural changes that can result in substantial improvements to the current procedures. These will impact the ongoing discourse on maritime safety by providing insights for policymakers, vessel operators, emergency planners, etc., and emphasize the need for further research and development efforts to fortify the industry against evolving safety challenges. Full article

Petrochemical feedstocks are experiencing a fast growth in demand, which will further expand their market in the coming years. This is due to an increase in the demand for petrochemical-based materials that are used in households, hospitals, transportation, electronics, and telecommunications. Consequently, petrochemical industries rely heavily on olefins, namely propylene, ethylene, and butene, as fundamental components for their manufacturing processes. Presently, there is a growing interest among refineries in prioritising their operations towards the production of fuels, specifically gasoline, diesel, and light olefins. The cost-effectiveness and availability of petrochemical primary feedstocks, such as propylene and butene, can be enhanced through the direct conversion of crude oil into light olefins using fluid catalytic cracking (FCC). To achieve this objective, the FCC technology, process optimisation, and catalyst modifications may need to be redesigned. It is helpful to know that there are several documented methods of modifying traditional FCC catalysts’ physicochemical characteristics to enhance their selectivity toward light olefins’ production, since the direct cracking of crude oil to olefins is still in its infancy. Based on a review of the existing zeolite catalysts, this work focuses on the factors that need to be optimized and the approaches to modifying FCC catalysts to maximize light olefin production from crude oil conversion via FCC. Several viewpoints have been combined as a result of this research, and recommendations have been made for future work in the areas of optimising the yield of light olefins by engineering the pore structure of zeolite catalysts, reducing deactivation by adding dopants, and conducting technoeconomic analyses of direct crude oil cracking to produce light olefins. Full article

Emotion classification using physiological signals is a promising approach that is likely to become the most prevalent method. Bio-signals such as those derived from Electrocardiograms (ECGs) and the Galvanic Skin Response (GSR) are more reliable than facial and voice recognition signals because they are not influenced by the participant’s subjective perception. However, the precision of emotion classification with ECG and GSR signals is not satisfactory, and new methods need to be developed to improve it. In addition, the fusion of the time and frequency features of ECG and GSR signals should be explored to increase classification accuracy. Therefore, we propose a novel technique for emotion classification that exploits the early fusion of ECG and GSR features extracted from data in the AMIGOS database. To validate the performance of the model, we used various machine learning classifiers, such as Support Vector Machine (SVM), Decision Tree, Random Forest (RF), and K-Nearest Neighbor (KNN) classifiers. The KNN classifier gives the highest accuracy for Valence and Arousal, with 69% and 70% for ECG and 96% and 94% for GSR, respectively. The mutual information technique of feature selection and KNN for classification outperformed the performance of other classifiers. Interestingly, the classification accuracy for the GSR was higher than for the ECG, indicating that the GSR is the preferred modality for emotion detection. Moreover, the fusion of features significantly enhances the accuracy of classification in comparison to the ECG. Overall, our findings demonstrate that the proposed model based on the multiple modalities is suitable for classifying emotions. Full article

Many beneficial proteins have limited natural availability, which often restricts their supply and thereby reduces their potential for therapeutic or industrial usage. The advent of recombinant DNA (rDNA) technology enables the utilization of different microbes as surrogate hosts to facilitate the production of these proteins. This microbial technology continues to evolve and integrate with modern innovations to develop more effective approaches for increasing the production of recombinant biopharmaceuticals. These strategies encompass fermentation technology, metabolic engineering, the deployment of strong promoters, novel vector elements such as inducers and enhancers, protein tags, secretion signals, synthetic biology, high-throughput devices for cloning, and process screening. This appraisal commences with a general overview regarding the manufacture of recombinant proteins by microbes and the production of biopharmaceuticals, their trends towards the development of biopharmaceuticals, and then discusses the approaches adopted for accomplishing this. The design of the upstream process, which also involves host selection, vector design, and promoter design, is a crucial component of production strategies. On the other hand, the downstream process focuses on extraction and purification techniques. Additionally, the review covers the most modern tools and resources, methods for overcoming low expression, the cost of producing biopharmaceuticals in microbes, and readily available recombinant protein products. Full article

The Bryonia genus (Cucurbitaceae) is divided into 13 plants considered medicinal species with a significant pharmacological value fortreating as well as preventing various ailments. The current systematic review aims to present useful and updated findings published onthis genus inthe last two decades. Based on PubMed, Science Direct, JSTOR, and Google Scholar, 42 of the available previous studies on Bryonia have been selected from 2000 to 2022. Thereafter, these studies were analyzed, summarized, and separately recorded according to the topic or section, adding some comments foreach. Our review provided a botanical description of the genus, followed by itsindigenous uses. Furthermore, more than 150 reported phytochemical compounds were grouped into families such as terpenoids, alkaloids, flavonoids, glycosides, saponins, and volatile oils. Hereby, thebiological activities part of this genus wereexposed, including itsantimicrobial, antioxidant, antidiabetic, antinociceptive, and anti-inflammatory functions, along with an interesting anticancer efficiency. Overall, our findings could contribute to forthcoming investigations that may lead to determining the responsible phytoconstituents for Bryonia’s efficiency. Full article

Microorganisms play a fundamental role in sustainable agriculture, and their importance in common bean (Phaseolus vulgaris) cultivation cannot be underestimated. This review article aims to comprehensively explore the diverse roles of microorganisms in sustainable biofortified common bean cultivation. Biofortification refers to the process of increasing the nutrient content in crops, which helps combat deficiencies in iron, zinc, and vitamins in the human body. Biofortified beans have better agronomic characteristics and offer higher micronutrient content compared to conventional crops. We examine the contribution of various microbial communities in nitrogen fixation, soil structure improvement, nutrient recycling, and disease suppression. Understanding the interaction between beneficial microorganisms and biofortified common bean plants enables us to develop ecologically sound and sustainable approaches to optimize crop productivity and improve nutrition and livelihoods for millions of people worldwide while reducing the environmental impact of agricultural practices. Full article

The environmental physiognomy of an area can significantly diminish its aesthetic appeal, rendering it susceptible to visual pollution, the unbeaten scourge of modern urbanization. In this study, we propose using a deep learning network and a robotic vision system integrated with Google Street View to identify streets and textile-based visual pollution in Dhaka, the megacity of Bangladesh. The issue of visual pollution extends to the global apparel and textile industry, as well as to various common urban elements such as billboards, bricks, construction materials, street litter, communication towers, and entangled electric wires. Our data collection encompasses a wide array of visual pollution elements, including images of towers, cables, construction materials, street litter, cloth dumps, dyeing materials, and bricks. We employ two open-source tools to prepare and label our dataset: LabelImg and Roboflow. We develop multiple neural network models to swiftly and accurately identify and classify visual pollutants in this work, including Faster SegFormer, YOLOv5, YOLOv7, and EfficientDet. The tuna swarm optimization technique has been used to select the applied models’ final layers and corresponding hyperparameters. In terms of hardware, our proposed system comprises a Xiaomi-CMSXJ22A web camera, a 3.5-inch touchscreen display, and a Raspberry Pi 4B microcontroller. Subsequently, we program the microcontroller with the YOLOv5 model. Rigorous testing and trials are conducted on these deep learning models to evaluate their performance against various metrics, including accuracy, recall, regularization and classification losses, mAP, precision, and more. The proposed system for detecting and categorizing visual pollution within the textile industry and urban environments has achieved notable results. Notably, the YOLOv5 and YOLOv7 models achieved 98% and 92% detection accuracies, respectively. Finally, the YOLOv5 technique has been deployed into the Raspberry Pi edge device for instantaneous visual pollution detection. The proposed visual pollutants detection device can be easily mounted on various platforms (like vehicles or drones) and deployed in different urban environments for on-site, real-time monitoring. This mobility is crucial for comprehensive street-level data collection, potentially engaging local communities, schools, and universities in understanding and participating in environmental monitoring efforts. The comprehensive dataset on visual pollution will be published in the journal following the acceptance of our manuscript. Full article

This study explored whether consistent differences can be found between early-wood and late-wood in terms of elemental content of tree rings. The species to study was Pinus sylvestris L. growing within an even-aged stand planted during the early 1970s in eastern Siberia. The wood specimens were extracted from the north and south sides of trees and subsequently scanned through an X-ray fluorescent facility Itrax Multiscanner. A sequence of relatively wide tree-rings was chosen for the analysis. The scanning data on a number of elements (Al, Si, P, S, Cl, K, Ca, Ti, Mn, Fe, Cu, Zn, Sr, and Hg) were split into early-wood and late-wood data for each year of growth. The early- and late-wood data in the same ring were analyzed for basic statistics against each other as well as against available meteorological data. In the northern direction, the elements Al, Si, P, Cl, Cu, and Zn are always more abundant in the late-wood, while Ca, Fe, and Sr are always more abundant in the early-wood. What is important is how the differences for P, Ca, Fe, Cu, Zn, and Sr were always significant. The calcium content in the early-wood was the most consistently reflective regarding the meteorological data for the early summer (June). In some trees, the late-wood K content was well correlated with the Vysotskii–Ivanov climatic index. In the southern direction, Cu and Zn were always more abundant in the late-wood, while Sr was more abundant in the early-wood. The differences for all three elements were always significant. The cases of consistent relationships, though rare, help to develop a research program in the area of dendrochemistry. Full article

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey study offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases—highlighting the emergent issue of generative AI bias, where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on perpetuating inequalities and reinforcing harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discuss the ethical considerations of their implementation, and emphasize the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these. Addressing bias in AI requires a holistic approach involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the emerging field of generative AI. Full article

Current advancements in the technology of the Internet of Things (IoT) have led to the proliferation of various applications in the healthcare sector that use IoT. Recently, it has been shown that voice signal data of the respiratory system (i.e., breathing, coughing, and speech) can be processed through machine learning techniques to detect different diseases of this system such as COVID-19, considered an ongoing global pandemic. Therefore, this paper presents a new IoT framework for the identification of COVID-19 based on breathing voice samples. Using IoT devices, voice samples were captured and transmitted to the cloud, where they were analyzed and processed using machine learning techniques such as the naïve Bayes (NB) algorithm. In addition, the performance of the NB algorithm was assessed based on accuracy, sensitivity, specificity, precision, F-Measure, and G-Mean. The experimental findings showed that the proposed NB algorithm achieved 82.97% accuracy, 75.86% sensitivity, 94.44% specificity, 95.65% precision, 84.61% F-Measure, and 84.64% G-Mean. Full article

The processes of hydrogen reduction of silicon and germanium chlorides under the conditions of high-frequency (40.68 MHz) counteracted arc discharge stabilized between two rod electrodes are investigated. The main gas-phase and solid products of plasma-chemical transformations are determined. Thermodynamic analysis of SiCl₄ + H₂ and GeCl₄ + H₂ systems for optimal process parameters was carried out. Using the example of hydrogen reduction of SiCl₄ by the method of numerical modeling, gas-dynamic and thermal processes for this type of discharge are investigated. The impurity composition of gas-phase and solid reaction products is investigated. The possibility of single-stage production of high-purity Si and Ge mainly in the form of compact ingots, as well as high-purity chlorosilanes and trichlorogermane, is shown. Full article