The acquisition of cancer hallmarks requires molecular alterations at multiple levels including genome, epigenome, transcriptome, proteome, and metabolome. In the past decade, numerous attempts have ...been made to untangle the molecular mechanisms of carcinogenesis involving single OMICS approaches such as scanning the genome for cancer-specific mutations and identifying altered epigenetic-landscapes within cancer cells or by exploring the differential expression of mRNA and protein through transcriptomics and proteomics techniques, respectively. While these single-level OMICS approaches have contributed towards the identification of cancer-specific mutations, epigenetic alterations, and molecular subtyping of tumors based on gene/protein-expression, they lack the resolving-power to establish the casual relationship between molecular signatures and the phenotypic manifestation of cancer hallmarks. In contrast, the multi-OMICS approaches involving the interrogation of the cancer cells/tissues in multiple dimensions have the potential to uncover the intricate molecular mechanism underlying different phenotypic manifestations of cancer hallmarks such as metastasis and angiogenesis. Moreover, multi-OMICS approaches can be used to dissect the cellular response to chemo- or immunotherapy as well as discover molecular candidates with diagnostic/prognostic value. In this review, we focused on the applications of different multi-OMICS approaches in the field of cancer research and discussed how these approaches are shaping the field of personalized oncomedicine. We have highlighted pioneering studies from “The Cancer Genome Atlas (TCGA)” consortium encompassing integrated OMICS analysis of over 11,000 tumors from 33 most prevalent forms of cancer. Accumulation of huge cancer-specific multi-OMICS data in repositories like TCGA provides a unique opportunity for the systems biology approach to tackle the complexity of cancer cells through the unification of experimental data and computational/mathematical models. In future, systems biology based approach is likely to predict the phenotypic changes of cancer cells upon chemo-/immunotherapy treatment. This review is sought to encourage investigators to bring these different approaches together for interrogating cancer at molecular, cellular, and systems levels.
A paradigm shift in power systems is observed due to the massive integration of renewable energy sources (RESs) as distributed generators. Mainly, solar photovoltaic (PV) panels and wind generators ...are extensively integrated with the modern power system to facilitate green efforts in the electrical energy sector. However, integrating these RESs destabilizes the frequency of the modern power system. Hitherto, the frequency control has not drawn sufficient attention due to the reduced inertia and complex control of power electronic converters associated with renewable energy conversion systems. Thus, this article provides a critical summary on the frequency control of solar PV and wind-integrated systems. The frequency control issues with advanced techniques, including inertia emulation, de-loading, and grid-forming, are summarized. Moreover, several cutting-edge devices in frequency control are outlined. The advantages and disadvantages of different approaches to control the frequency of high-level RESs integrated systems are well documented. The possible improvements of existing approaches are outlined. The key research areas are identified, and future research directions are mentioned so that cutting-edge technologies can be adopted, making the review article unique compared to the existing reviews. The article could be an excellent foundation and guidance for industry personnel, researchers, and academicians.
Controlling population expansion and reducing unintended pregnancies through the use of modern contraceptives is a cost-effective strategy. In recent years, the rate of modern contraceptive use in ...Bangladesh has been declining. So, this study aimed to investigate the associated factors of the deterioration in modern contraceptive usage.
This study used data from two successive Bangladesh Demographic and Health Surveys (2014 and 2017-18) and applied the Blinder-Oaxaca decomposition analysis to understand the drivers. A popular binary logistic regression model is fitted to determine the factors that influence the use of modern contraceptive methods over the years.
This study revealed that highly educated women were more likely to use modern contraception methods, and their use increased by 3 percent over the years. Factors such as women's working status, husband's education, number of living children, and fertility preference were found significantly associated with decreased usage of modern contraception methods over years. The result of the Blinder-Oaxaca (BO) decomposition analysis found a significant decrease between 2014 and 2018. Respondent's age, working status, husband's age, opinion on decision making, region, and media exposure were the most significant contributors to explaining the shift between 2014 and 2018. The two factors that contributed most to narrowing the difference between the two surveys were women's decision on own health (26%), and employment status (35%).
The factors that influence modern contraceptive prevalence are important to know for policy implication purposes in Bangladesh. The findings indicate the need for further improvement of factors for balancing the usage of modern contraception methods.
We demonstrate a highly sensitive Au-MoS2-Graphene based hybrid surface plasmon resonance (SPR) biosensor for the detection of DNA hybridization. The performance parameters of the proposed sensor are ...investigated in terms of sensitivity, detection accuracy and quality factor at operating wavelength of 633nm. We observed in the numerical study that sensitivity can be greatly increased by adding MoS2 layer in the middle of a Graphene-on-Au layer. It is shown that by using single layer of MoS2 in between gold and graphene layer, the proposed biosensor exhibits simultaneously high sensitivity of 87.8deg/RIU, high detection accuracy of 1.28 and quality factor of 17.56 with gold layer thickness of 50nm. This increased performance is due to the absorption ability and optical characteristics of graphene biomolecules and high fluorescence quenching ability of MoS2. On the basis of changing in SPR angle and minimum reflectance, the proposed sensor can sense nucleotides bonding happened between double-stranded DNA (dsDNA) helix structures. Therefore, this sensor can successfully detect the hybridization of target DNAs to the probe DNAs pre-immobilized on the Au-MoS2-Graphene hybrid with capability of distinguishing single-base mismatch.
•New biosensor is proposed using Molybdenum disulphide in between Gold and Graphene.•It uses angle interrogation method and exhibits high sensitivity of 89.29deg/RIU.•The reasonable Detection Accuracy and Quality Factor are obtained from this design.•Mathematical model and detection approach are discussed thoroughly.•This Au-MoS2-Graphene based biosensor can successfully detect the DNA hybridization.
Bangladesh positioned as third rice producing country in the world. In Bangladesh, regional growth and trend in rice production determinants, disparities and similarities of rice production ...environments are highly desirable. In this study, the secondary time series data of area, production, and yield of rice from 1969-70 to 2019-20 were used to investigate the growth and trend by periodic, regional, seasonal and total basis. Quality checking, trend fitting, and classification analysis were performed by the Durbin-Watson test, Exponential growth model, Cochrane-Orcutt iteration method and clustering method. The production contribution to the national rice production of Boro rice is increasing at 0.97% per year, where Aus and Aman season production contribution significantly decreased by 0.48% and 0.49% per year. Among the regions, Mymensingh, Rangpur, Bogura, Jashore, Rajshahi, and Chattogram contributed the most i.e., 13.9%, 9.8%, 8.6%, 8.6%, 8.2%, and 8.0%, respectively. Nationally, the area of Aus and Aman had a decreasing trend with a -3.63% and -0.16% per year, respectively. But, in the recent period (Period III) increasing trend was observed in the most regions. The Boro cultivation area is increasing with a rate of 3.57% per year during 1984-85 to 2019-20. High yielding variety adoption rate has increased over the period and in recent years it has found 72% for Aus, 73.5% for Aman, and 98.4% for Boro season. As a result, the yield of the Aus, Aman, and Boro seasons has been found increasing growth for most of the regions. We have identified different cluster regions in different seasons, indicating high dissimilarities among the rice production regions in Bangladesh. The region-wise actionable plan should be taken to rapidly adopt new varieties, management technologies and extension activities in lower contributor regions to improve productivity. Cluster-wise, policy strategies should be implemented for top and less contributor regions to ensure rice security of Bangladesh.
Reverse engineering is a burning issue in Integrated Circuit (IC) design and manufacturing. In the semiconductor industry, it results in a revenue loss of billions of dollars every year. In this ...work, an area efficient, high-performance IC camouflaging technique is proposed at the physical design level to combat the integrated circuit's reverse engineering. An attacker may not identify various logic gates in the layout due to similar image output. In addition, a dummy or true contact-based technique is implemented for optimum outcomes. A library of gates is proposed that contains the various camouflaged primitive gates developed by a combination of using the metal routing technique along with the dummy contact technique. This work shows the superiority of the proposed technique's performance matrix with those of existing works regarding resource burden, area, and delay. The proposed library is expected to make open source to help ASIC designers secure IC design and save colossal revenue loss.
Antimony (Sb) chalcogenides such as antimony selenide (Sb2Se3) and antimony sulfide (Sb2S3) have distinct properties to be used as absorber semiconductors for harnessing solar energy including high ...absorption coefficient, tunable bandgap, low toxicity, phase stability. The potentiality of Sb2Se3 and Sb2S3 as absorber material in Al/FTO/Sb2Se3(or Sb2S3)/Au heterojunction solar cells (HJSCs) with 2D tungsten disulfide (WS2) electron transport layer (ETL) layer has been investigated numerically using SCAPS-1D solar simulator. A systematic investigation of the impact of physical properties of each active material of Sb2Se3, Sb2S3, and WS2 on photovoltaic parameters including layer thickness, carrier doping concentration, bulk defect density, interface defect density, carrier generation, and recombination. This study emphasizes the exploration of causes of low performance of actual devices and demonstrates the individual variation in the open-circuit voltage (VOC), short-circuit current density (JSC), fill factor (FF), power conversion efficiency (PCE) and quantum efficiency (QE). Thereby, highly potential heterostructures of Al/FTO/WS2/absorber (Sb2Se3 or Sb2S3)/Au proposed, in which, the PCE over 28.20 and 26.60% obtained with VOC of 850 and 1230 mV, Jsc of 38.0 and 24.0 mA/cm2, and FF of 86.0 and 89.0% for Sb2Se3 and Sb2S3 absorber, respectively. These detailed findings revealed that the Sb-chalcogenide heterostructure with potential WS2 ETL can be used to realize the fabrication of feasible thin film solar cells and thus the design of high-efficiency high-current (HEHC) and high-efficiency high-voltage (HEHV) solar panels.
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•Antimony chalcogenide (Sb2Se3 and Sb2Se3)-based TFSCs with WS2 electron transport layer were studied by SCAPS-1D simulator.•Systematic investigation on the impacts of thickness, doping, bulk, and interface defect densities on the PV performance.•PCE of 28.20% (26.60%) was found in a 1280 nm thick n+/n/p junction Sb2Se3 (Sb2S3) solar cell under adjusted condition.•The simulation was verified with the Shockley–Queisser (SQ) limit including experimental as well as simulation works.
Heterojunction solar cell; Sb2Se3; Sb2S3; WS2 electron transport layer; SCAPS-1D.
Inorganic CdTe and FeSi2-based solar cells have recently drawn a lot of attention because they offer superior thermal stability and good optoelectronic properties compared to conventional solar ...cells. In this work, a unique alternative technique is presented by using FeSi2 as a secondary absorber layer and In2S3 as the window layer for improving photovoltaic performance parameters. Simulating on SCAPS-1D, the proposed double-absorber (Cu/FTO/In2S3/CdTe/FeSi2/Ni) structure is thoroughly examined and analyzed. The window layer thickness, absorber layer thickness, acceptor density (NA), donor density (ND), defect density (Nt), series resistance (RS), and shunt resistance (Rsh) were simulated in detail for optimization of the above configuration to improve the PV performance. According to this study, 0.5 µm is the optimized thickness for both the CdTe and FeSi2 absorber layers in order to maximize the efficiency (η). Here, the value of the optimum window layer thickness is 50 nm. For using CdTe as a single absorber, η is achieved by 13.26%. However, for using CdTe and FeSi2 as a dual absorber, η is enhanced and the obtaining value is 27.35%. The other parameters are also improved and the resultant value for the fill factor is 83.68%, the open-circuit voltage (Voc) is 0.6566 V, and the short circuit current density (Jsc) is 49.78 mA/cm2. Furthermore, the proposed model performs well at 300 K operating temperature. The addition of the FeSi2 layer to the cell structure has resulted in a significant quantum efficiency enhancement because of the rise in solar spectrum absorption at longer wavelengths (λ). The findings of this work offer a promising approach for producing high-performance and reasonably priced CdTe-based solar cells.
Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as ...accuracy and mean average precision (mAP). Generally, a model's high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step. Massive emission of CO2 has a deleterious impact on life on earth in general and is a serious ethical concern that is largely ignored in deep learning research. In this article, we mainly focus on environmental costs and the means of mitigating carbon footprints in deep learning models, with a particular focus on models created using knowledge distillation (KD). Deep learning models typically contain a large number of parameters, resulting in a 'heavy' model. A heavy model scores high on performance metrics but is incompatible with mobile and edge computing devices. Model compression techniques such as knowledge distillation enable the creation of lightweight, deployable models for these low-resource devices. KD generates lighter models and typically performs with slightly less accuracy than the heavier teacher model (model accuracy by the teacher model on CIFAR 10, CIFAR 100, and TinyImageNet is 95.04%, 76.03%, and 63.39%; model accuracy by KD is 91.78%, 69.7%, and 60.49%). Although the distillation process makes models deployable on low-resource devices, they were found to consume an exorbitant amount of energy and have a substantial carbon footprint (15.8, 17.9, and 13.5 times more carbon compared to the corresponding teacher model). The enormous environmental cost is primarily attributable to the tuning of the hyperparameter, Temperature (τ). In this article, we propose measuring the environmental costs of deep learning work (in terms of GFLOPS in millions, energy consumption in kWh, and CO2 equivalent in grams). In order to create lightweight models with low environmental costs, we propose a straightforward yet effective method for selecting a hyperparameter (τ) using a stochastic approach for each training batch fed into the models. We applied knowledge distillation (including its data-free variant) to problems involving image classification and object detection. To evaluate the robustness of our method, we ran experiments on various datasets (CIFAR 10, CIFAR 100, Tiny ImageNet, and PASCAL VOC) and models (ResNet18, MobileNetV2, Wrn-40-2). Our novel approach reduces the environmental costs by a large margin by eliminating the requirement of expensive hyperparameter tuning without sacrificing performance. Empirical results on the CIFAR 10 dataset show that the stochastic technique achieves an accuracy of 91.67%, whereas tuning achieves an accuracy of 91.78%-however, the stochastic approach reduces the energy consumption and CO2 equivalent each by a factor of 19. Similar results have been obtained with CIFAR 100 and TinyImageNet dataset. This pattern is also observed in object detection classification on the PASCAL VOC dataset, where the tuning technique performs similarly to the stochastic technique, with a difference of 0.03% mAP favoring the stochastic technique while reducing the energy consumptions and CO2 emission each by a factor of 18.5.
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