Enhanced photocatalytic property of Cu doped Ce2Zr2O7 toward photodegradation of methylene blue under visible light.

Widespread ecosystem degradation from noxious substances like industrial waste, toxic dyes, pesticides, and herbicides poses serious environmental risks. For remediation of these hazardous problems, present study introduces an innovative Cu-doped Ce2Zr2O7 nano-photocatalyst, fabricated via a simple, eco-friendly hydrothermal method, designed to degrade toxic textile dye methylene blue. Harnessing Cu doping for pyrochlore Ce2Zr2O7, structure engineering carried out through a hydrothermal synthesis method to achieve superior photocatalytic performance, addressing limitations of rapid charge carrier recombination in existing photocatalysts. Photoluminescence analysis showed that doped pyrochlore slows charge carrier recombination, boosting dye degradation efficiency. UV-Visible analysis demonstrated an impressive 96 % degradation of methylene blue by Cu-doped Ce2Zr2O7 within 50 min, far exceeding the performance of pristine materials. Trapping experiments clarified the charge transfer mechanism, deepening our understanding of the photocatalytic process. These findings highlight the potential for developing innovative, highly efficient photocatalysts for environmental remediation, offering sustainable solutions to combat pollution. This study not only addresses the limitations of existing photocatalysts but also opens new avenues for enhancing photocatalytic performance through strategic material design.

A review on cultivating effective learning: synthesizing educational theories and virtual reality for enhanced educational experiences.

Immersive technology, especially virtual reality (VR), transforms education. It offers immersive and interactive learning experiences. This study presents a systematic review focusing on VR's integration with educational theories in higher education. The review evaluates the literature on VR applications combined with pedagogical frameworks. It aims to identify effective strategies for enhancing educational experiences through VR. The process involved analyzing studies about VR and educational theories, focusing on methodologies, outcomes, and effectiveness. Findings show that VR improves learning outcomes when aligned with theories such as constructivism, experiential learning, and collaborative learning. These integrations offer personalized, immersive, and interactive learning experiences. The study highlights the importance of incorporating educational principles into VR application development. It suggests a promising direction for future research and implementation in education. This approach aims to maximize VR's pedagogical value, enhancing learning outcomes across educational settings.

Enhancing Tensile Strength, Thermal Stability, and Antioxidant Characteristics of Transparent Kappa Carrageenan Films Using Grapefruit Essential Oil for Food Packaging Applications.

The trends in food packaging technologies are shifting toward utilizing natural and environmentally friendly materials prepared from biopolymers such as kappa carrageenan to replace synthetic polymers. In the current study, varying amounts (0.1, 0.2, and 0.3%) of grapefruit essential oil (GFO) were incorporated in kappa carrageenan-based edible films to improve their physicochemical properties. The developed film samples were characterized for their barrier, mechanical, morphological, optical, thermal, antioxidant, and biodegradable properties. The results obtained showed that the tensile strength of the carrageenan films enhanced significantly from 65.20 ± 4.71 to 98.21 ± 6.35 MPa with the incorporation of GFO in a concentration-dependent manner. FTIR and SEM analysis confirmed the intermolecular bonding between carrageenan and GFO, resulting in the formation of compact films. Incorporating GFO significantly enhanced the thermal resistance of oil-loaded films, as confirmed by TGA, DSC, and DTG analysis. The addition of GFO led to a substantial increase in the radical scavenging activity of the films, as evidenced by the DPPH and ABTS assays. Furthermore, the developed films were biodegradable in soil and seawater environments, indicating their potential as a sustainable alternative to traditional plastics. Findings demonstrated that GFO can be used as a natural antioxidant agent in kappa carrageenan-based films for potential applications in food packaging.

Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain.

Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city's efficient operation. The idea behind "smart cities" is that information and communication technology (ICTs) need to be included in a city's routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The "drone" or "uncrewed aerial vehicle" (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.

Novel breath biomarkers identification for early detection of hepatocellular carcinoma and cirrhosis using ML tools and GCMS.

According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.

Encapsulation of probiotic bacteria using polyelectrolytes stabilized nanoliposomes for improved viability under hostile conditions.

Probiotics viability and stability is a core challenge for the food processing industry. To prolong the viability of probiotics (Lactobacillus acidophilus), gelatin (GE)-chitosan (CH) polyelectrolytes-coated nanoliposomes were developed and characterized. The average particle size of the nanoliposomes was in the range of 131.7-431.6 nm. The mean zeta potential value of the nanoliposomes differed significantly from -42.2 to -9.1 mV. Scanning electron micrographs indicated that the nanoliposomes were well distributed and had a spherical shape with a smooth surface. The Fourier transform infrared spectra revealed that the GE-CH polyelectrolyte coating has been effectively applied on the surface of nanoliposomes and L. acidophilus cells were successfully encapsulated in the lipid-based nanocarriers. X-ray diffraction results indicated that nanoliposomes are semicrystalline and GE-CH polyelectrolyte coating had an influence on the crystalline nature of nanoliposomes. Moreover, the coating of L. acidophilus-loaded nanoliposomes with GE-CH polyelectrolytes significantly improved its viability when exposed to simulated gastrointestinal environments. The findings of the current study indicated that polyelectrolyte-coated nanoliposomes could be used as an effective carrier for the delivery of probiotics and their application to food matrix for manufacturing functional foods.

Chronic exposure to electronic waste poses risk to liver toxicity with molecular interaction of GSTM1, GSTT1 null variants, and GSTP1.

Chronic exposure to electronic waste (e-waste) is becoming a serious concern for health among individuals exposed to it. E-waste has been reported to contain heavy metals, trace elements, and persistent organic pollutants which can trigger health issues through different biological pathways. The liver is a major metabolic and detoxifying organ in the body. Glutathione S-transferase (GST) is a liver enzyme for phase II detoxification that catalyzes glutathione (GSH) conjugation with environmental pollutants. This research aimed to investigate the liver toxicity caused by long-term exposure to e-wastes, exploring the potential association with null variants of GSTT1 and GSTMI, as well as GSTP1. The study was designed as a cross-sectional investigation, in which 256 adult males who were chronically exposed to e-waste and 200 non-exposed control participants, matched for age and gender, were recruited randomly. Standard colorimetric and enzymatic methods were used to analyze biochemical parameters such as serum alkaline phosphatase (ALP), alanine transaminase (ALT), total bilirubin (T. Bil), albumin, and reduced glutathione. Genotypic analysis of the null variant GSTM1, GSTT1, and GSTP1 genes was conducted by standard molecular methods. The study findings indicated a notable surge in ALP, ALT, and albumin levels while T. Bil and GSH levels showed a reduction, suggesting a potential risk of liver toxicity. Additionally, analysis of GSTM1, GSTT1, and GSTP1 genotypes revealed a possible association with GSH levels and the hepatotoxicity risk. The study concluded that the individuals exposed to e-waste exhibited dysregulation of liver enzymes that results in liver toxicity. Moreover, analysis of GSTM1, GSTT1, and GSTP1 at a molecular level revealed that these genes could potentially serve as risk factors for liver toxicity in e-waste chronic exposure.

Does FDI foster technological innovations? Empirical evidence from BRICS economies.

The idea behind the spillover effect of FDI on economic growth is based on the idea that multinational companies can bring technological innovation and rich knowledge to host countries. Therefore, FDI plays a vital role in technological innovations. This study aims to investigate the impact of foreign direct investment (FDI) on the technological innovation of BRICS countries from 2000 to 2020. This study uses the latest econometric techniques, such as the cross-sectional dependence (CD) test, second-generation unit root tests, panel cointegration tests and the Dumitrescu-Hurlin causality test. For long-run run estimation, this study uses the augmented mean group (AMG) panel estimator and the common correlated effects mean group (CCEMG) estimator for empirical analysis. The findings of the study show that foreign direct investment (FDI), trade openness, economic growth, and research & development expenditure positively impact technological innovation in BRICS countries. Also, the model's long-term causality and lagged error correction term (ECT) are significantly negative. Suggested policy measures will be helpful for BRICS economies in boosting technology innovation through FDI.

Correction: Nanoscale imaging of antiferromagnetic domains in epitaxial films of Cr2O3 via scanning diamond magnetic probe microscopy.

[This corrects the article DOI: 10.1039/D2RA06440E.].

Identification of phenol 2,2-methylene bis, 6 [1,1-D] as breath biomarker of hepatocellular carcinoma (HCC) patients and its electrochemical sensing: E-nose biosensor for HCC.

BACKGROUND: According to WHO, Hepatocellular cancer (HCC) was the second leading cause of death in 2019 and is gradually increasing. The lipid peroxidation mechanism in cancer cells causes the emission of VOCs in the breath. Volatile organic compounds (VOCs) in breath are becoming favorable biomarkers, especially for cancers, for their sample retrieval and specific association with early metabolic changes. Since both diagnosis and prognosis of the disease depend on the quantity and kind of circulatory biomarkers to be detected, sensitive and selective biosensors with the possibility for portability are constantly in demand. RESULTS: In this study, breath samples of HCC patients were screened for identification of VOCs via GCMS and later verified by applying unsupervised machine learning models. Phenol 2,2 methylene bis [6-(1,1-dimethyl ethyl)-4-methyl] (MBMBP) was found to be significant VOC in the breath of HCC patients, with a minimum concentration of 2100 ppm. Thiol-modified AuNPs were synthesized, as we reported earlier, and immobilized on the working electrode surface to electrochemically sense MBMBP in purified form and later from clinical breath samples. During the electrochemical experiment of AuNPs with MPMBP, the analyte gets electro-oxidized, whereas the Au (III) ions get reduced to the phenoxy radical's species. The electrochemical analysis of MBMBP detection using hexane thiol AuNPs showed a LOD of 0.005 molL 1. The thiolated AuNPs-based biosensor for HCC diagnosis via VOC detection confirmed MPMBP in lab standards and raw clinical breath samples of HCC patients. SIGNIFICANCE: This study reveals that GCE modified with hexanethiol AuNPs for the adsorption of significant breath biomarker, is a potential platform for the development of e-nose sensor for the detection of HCC at early stage.

Towards portable rapid TB biosensor: Detecting Mycobacterium tuberculosis in raw sputum samples using functionalized screen printed electrodes.

Due to lack of robust, sensitive and low cost detection strategies, Tuberculosis (TB) remains a significant global health issue. WHO reports 1.5 million deaths per year, ∼80 % cases occur in low- to middle-income countries, where resource limitations complicate the diagnosis. Robust detection of TB infection is important to contain the spread and treat disease. We developed a label-free DNA biosensor based on commercially available screen printed electrodes (SPEs) (DropSens and Zensors) that can detect TB robustly, sensitively, and specifically via DNA hybridization with its IS6110 gene marker, in purified DNA and raw sputum samples. The fabricated biosensor was morphologically characterized by scanning electron microscopy coupled with energy dispersive X-ray spectroscopy. Cyclic voltammetry and Differential Pulse Voltammetry was used for electrochemical analysis of the modified electrode. The fabricated biosensor demonstrated satisfactory selectivity for Mycobacterium tuberculosis (MTB) against Salmonella typhimurium and Escherichia coli and was able to detect MTB; the limit of detection (LOD) of 1.90 nM with R2 = 0.993, when analyzed over a range of concentrations of DNA (0.5-10 nM). It is being exploited to detect target MTB from clinical samples, without DNA purification. The approach is robust, sensitive, and specific, requires low sample volume and can be extended towards portable point of care diagnosis of TB.

Dysregulation of butyrylcholinesterase, BCHE gene SNP rs1803274, and pro-inflammatory cytokines in occupational workers.

BACKGROUND: People in different occupations are exposed to a variety of xenobiotics which affect the health and physiological processes of the body. Butyrylcholinesterase (BChE), has been reported to play neuronal and non-neuronal roles, though its exact function is yet to be established. This study aimed to find the status and role of BChE in seven different occupational groups; gasoline fillers, auto-mechanics, carpenters, textile shop workers, furniture shop workers, electricians, and office workers. METHODS: A total of 400 samples were screened. BChE activity was determined by Worek et al. method based on Ellman's principle. Pro-inflammatory cytokines were determined by ELISA. Genotypic analysis of the K-variant of BCHE gene SNP was carried out by standard molecular methods. Among seven groups, office workers were taken as a control to compare the results with all other occupational groups. RESULTS: The results revealed a significant decrease in BChE activity in gasoline fillers (79.52%) followed by carpenters (73.49%), auto mechanics (39.76%), textile shop workers (18.07%), electricians (10.84%), and furniture shop workers (7.23%). TNF-α, IL-6, and IL1-ß were elevated in all groups. IL-6 and IL1-ß in gasoline fillers, and electricians were not statistically significantly increased. Binomial regression to determine the odd ratio was found to be significant (p < 0.05) in all groups. However, correlation (Pearson) did not reveal significance between different biochemical parameters. Genotypic analysis of the K-variant SNP of the BCHE gene showed a significant association with occupational groups when compared with control which indicates a possible association with xenobiotics exposure and the physiological role of K-variant in understudied occupational groups. CONCLUSION: The study concluded that BChE and its gene SNP rs 1803274 and proinflammatory cytokines significantly dysregulates under the exposure to cumulative multiple xenobiotics in different occupational groups which may lead to pathophysiological conditions.

The symmetric and asymmetric impacts of green energy, eco-innovation, and urbanization in explaining low-carbon economy for Pakistan.

Over the past three decades, global economic development patterns have considerably affected the natural environment, and economies have endured a plethora of environmental concerns as a result of the negative effects of climate change. Among them, Pakistan is the fifth most vulnerable country, and climate change has harmfully affected the ecological and socio-economic conditions of the country. In this regard, this study aimed to investigate the role of green energy consumption, eco-innovation, and urbanization while explaining the dream of low-carbon economy and environmental sustainability in the context of Pakistan using annual time series dataset spanning from 1990 to 2020. The short-run and long-run associations among explained and explanatory variables were investigated using the symmetric, asymmetric, and quantile autoregressive distributed lag models. The findings of the study demonstrated that low-carbon economy, green energy consumption, ecological innovation, urbanization, GDP per capita, and labor force are cointegrated for the long-term association in symmetric, asymmetric, and quantile autoregressive distributed lag models. Furthermore, green energy consumption and effective eco-innovation are the most important paths to ensure environmental sustainability, while urbanization, GDP per capita, and labor force contribute negatively to the low-carbon economy. The findings of the study provide a policy framework for the development of a comprehensive strategy to promote environmental sustainability in Pakistan by emphasizing green energy consumption, ecological innovation, and controlled urbanization, as well as the incorporation of environment friendly policies into economic development policies.

Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach.

Cyclin-dependent kinase inhibitor 2 A (CDKN2A) gene belongs to the cyclin-dependent kinase family that code for two transcripts (p16INK4A and p14ARF), both work as tumor suppressors proteins. The mutation that occurs in the p14ARF protein can lead to different types of cancers. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that can lead to different types of diseases. In this study, we applied the computational strategy on human p14ARF protein to identify the potential deleterious nsSNPs and check their impact on the structure, function, and protein stability. We applied more than ten prediction tools to screen the retrieved 288 nsSNPs, consequently extracting four deleterious nsSNPs i.e., rs139725688 (R10G), rs139725688 (R21W), rs374360796 (F23L) and rs747717236 (L124R). Homology modeling, conservation and conformational analysis of mutant models were performed to examine the divergence of these variants from the native p14ARF structure. All-atom molecular dynamics simulation revealed a significant impact of these mutations on protein stability, compactness, globularity, solvent accessibility and secondary structure elements. Protein-protein interactions indicated that p14ARF operates as a hub linking clusters of different proteins and that changes in p14ARF may result in the disassociation of numerous signal cascades. Our current study is the first survey of computational analysis on p14ARF protein that determines the association of these nsSNPs with the altered function of p14ARF protein and leads to the development of various types of cancers. This research proposes the described functional SNPs as possible targets for proteomic investigations, diagnostic procedures, and treatments.Communicated by Ramaswamy H. Sarma.

Exploring the links between fossil fuel energy consumption, industrial value-added, and carbon emissions in G20 countries.

The primary objective of this study is to explore the links between fossil fuel energy consumption, industrial value-added, and carbon emissions in G20 countries over the period 1990-2020. Panel unit root test, co-integration test, and CS-ARDL estimator were used to determine the relationship among variables. The empirical results suggest that the driving force of carbon emissions in G20 countries varies significantly in advanced versus emerging economies. Evidence in a whole sample of G20 countries and advanced economies supports environmental Kuznets curve (EKC) hypothesis, while no evidence  emerging economies supports EKC hypothesis. Apart from this, the empirical results show trade opens, FDI, government expenditures on health and education, research and development, and information and communication technology are other determinators of carbon emissions in G20 countries. Our results suggest that countries upgrade industrial structures by shifting their energy structures away from fossil fuels toward renewable energy sources in order to achieve sustainable environmental goals.

Contrast enhanced sonothrombolysis using streptokinase loaded phase change nano-droplets for potential treatment of deep venous thrombosis.

Current thrombolytic therapies for deep venous thrombosis are limited due to the wide side effect profile. Contrast mediated sonothrombolysis is a promising approach for thrombus treatment. The current study examines the effectiveness of in vitro streptokinase (SK) loaded phase-change nanodroplet (PCND) mediated sonothrombolysis at 7 MHz for the diagnosis of deep venous thrombosis. Lecithin shell and perfluorohexane core nanodroplets were prepared via the thin-film hydration method and morphologically characterized. Sonothrombolysis was performed at 7 MHz at different mechanical indexes of samples i.e., only sonothrombolysis, PCND mediated sonothrombolysis, sonothrombolysis with SK and SK loaded PCND mediated sonothrombolysis. Thrombolysis efficacy was assessed by measuring clot weight changes during 30 min US exposure, recording the mean gray intensity from the US images of the clot by computer software ImageJ, and spectrophotometric quantification of the hemoglobin in the clot lysate. In 15 minutes of sonothrombolysis performed at high mechanical index (0.9 and 1.2), SK loaded PCNDs showed a 48.61% and 74.29% reduction of mean gray intensity. At 0.9 and 1.2 MI, 86% and 92% weight loss was noted for SK-loaded PCNDs in confidence with spectrophotometric results. A significant difference (P < 0.05) was noted for SK-loaded PCND mediated sonothrombolysis compared to other groups. Loading of SK inside the PCNDs enhanced the efficacy of sonothrombolysis. An increase in MI and time also increased the efficacy of sonothrombolysis. This in vitro study showed the potential use of SK-loaded perfluorohexane core PCNDs as sonothrombolytic agents for deep venous thrombosis.

KIF1A novel frameshift variant p.(Ser887Profs*64) exhibits clinical heterogeneity in a Pakistani family with hereditary sensory and autonomic neuropathy type IIC.

Background: Hereditary sensory and autonomic neuropathies (HSANs) are rare heterogeneous group of neurological disorders caused by peripheral nerve deterioration. The HSANs sub-clinical classes have clinical and genetic overlap which often lead to misdiagnosis. In the present study a Pakistani family with five affected members suffering from severe neuropathy were genetically analyzed to identify the disease causative element in the family.Methods: Genome wide high-density single nucleotide polymorphism (SNP) microarray analysis was carried out followed by whole exome sequencing of the affected proband and another affected sibling. Shared homozygous regions in all severely affected members were identified through homozygosity mapping approach.Results: The largest homozygous region of 14.1 Mb shared by the five severely affected members of the family was identified on chromosome 2. Subsequent exome sequencing identified a novel single nucleotide deletion c.2658del; p.(Ser887Profs*64) in KIF1A. Segregation analysis revealed that this mutation was homozygous in all five affected individuals of the family with severe clinical manifestation, while members of the family that were heterozygous carriers shared abnormal skin features (scaly skin) only with the homozygous affected members.Conclusions: A novel frameshift mutation p.(Ser887Profs*64) in KIF1A is the potential cause of severe HSANIIC in a Pakistani family along with incomplete penetrance in mutation carriers. We demonstrate that using a combination of different techniques not only strengthens the gene finding approach but also helps in proper sub-clinical characterization along with identification of mutated alleles exhibiting incomplete penetrance leading to intrafamilial clinical variability in HSAN group of inherited diseases.

Novel Magnetic Elastic Phase-Change Nanodroplets as Dual Mode Contrast Agent for Ultrasound and Magnetic Resonance Imaging.

Recently, dual-mode imaging systems merging magnetic resonance imaging (MRI) and ultrasound (US) have been developed. Designing a dual-mode contrast agent is complex due to different mechanisms of enhancement. Herein, we describe novel phase change nanodroplets (PCNDs) with perfluoropentane encapsulated in a pre-polyglycerol sebacate (pre-PGS) shell loaded with polyethylene glycol (PEG)-coated iron oxide nanoparticles as having a dual-mode contrast agent effect. Iron oxide nanoparticles were prepared via the chemical co-precipitation method and PCNDs were prepared via the solvent displacement technique. PCNDs showed excellent enhancement in the in vitro US much more than Sonovue® microbubbles. Furthermore, they caused a susceptibility effect resulting in a reduction of signal intensity on MRI. An increase in the concentration of nanoparticles caused an increase in the MR contrast effect but a reduction in US intensity. The concentration of nanoparticles in a shell of PCNDs was optimized to obtain a dual-mode contrast effect. Biocompatibility, hemocompatibility, and immunogenicity assays showed that PCNDs were safe and non-immunogenic. Another finding was the dual-mode potential of unloaded PCNDs as T1 MR and US contrast agents. Results suggest the excellent potential of these PCNDs for use as dual-mode contrast agents for both MRI and US.

Making Cities Smarter-Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints.

One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.

A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition.

Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%.