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Black sooty-like materials were observed in the sands of beach of the Waterfront areas of the Royal Commission at Yanbu – facing the Red Sea. Two kinds of samples of beach sands were collected. One sample was the blackish sooty-Like material marked as “contaminated sample” and the other was the clean or normal or uncontaminated sand and it was marked as “controlled sample”. The samples of both types of beach sands were investigated to determining the content of oil and the amount of organic matter in them. The oil content in the “contaminated sample” was determined to be about 20 times higher than the value in uncontaminated or the “controlled sample” of the beach sands. The organic matter content value in the “contaminated sample” although determined to be on a little higher side – as compared to those found in the “controlled sample” – however this value was determined to be less than 4 to 6% by weight of dry sample as is usually observed in a typical beach sand. Based on the physical and chemical analysis of the “contaminated” beach sands and comparing them with the “controlled sample”, it was safe to infer that these blackish sooty-like materials found in beach sites were due to some leaked or spilt oil in the Red Sea that were washed ashore during the high tide periods.

Nitrous oxide (N2O), a colorless gas known to have abuse potential, can induce vitamin B12 deficiency that eventually leads to peripheral neuropathy, central nervous demyelination, and myelopathy. N2O abuse has rarely caused subacute combined degeneration of the spinal cord despite being reported in a few studies. Although numerous published studies have demonstrated the toxic effects of N2O abuse, it is still a controversial topic of debate among neurologists. We outline a case of a patient presenting with acute onset of numbness who was ultimately diagnosed with myelopathy secondary to vitamin B12 deficiency induced by nitrous oxide abuse. This case report emphasizes the early diagnosis and management of vitamin B12 deficiency to prevent the severe complications associated with it.

Prolonged exposure of the earth's surface to the sun's ultraviolet radiation may result in various skin diseases and cataract. Carbazole (CBZ), as a polycyclic-aromatic hydrocarbon (PAH), is blended with a five-member nitrogen-containing ring. It is found in cigarette smoke, coal, eye kohl, tattoo ink, and wood combustion and affects various types of flora and fauna. Our findings suggest that CBZ generates reactive oxygen species (ROS) like O 2 • - through type-I photodynamic reaction and causes phototoxicity in the human keratinocyte cell line (HaCaT), which has been proved by mitochondrial dehydrogenase and neutral red uptake assays. CBZ induces single strand DNA damage. We have investigated the involvement of the apoptotic pattern of cell death and confirmed it by cytochrome C release from mitochondria and caspase-9 activation. Similarly, photo-micronuclei formation was associated to CBZ-induced phototoxicity. The results of this study strongly support that the upregulation of bax, cyto-C, apaf-1, casp-9 and down regulation of bcl2, keap-1, nrf-2, and hmox-1 genes cause apoptopic cell death. Downregulation of antioxidant genes showed a significant amount of ROS generation by photosensitized CBZ. Therefore, the current study will be a step forward to safeguard human beings from sunlight-induced photosensitive CBZ prolonged exposure.

Ensuring integrity and security of computer networks is one of the growing concerns. The number of malware specifically designed to damage, disrupt or perform illegitimate actions on data, networks or hosts are increasing day by day. Detection of hosts infected by malware known as bots is the main focus of this paper. While Botnets are an emerging threat with hundreds of millions of computers infected, the research and solutions of it are still in their infancy stage. In this paper, at first we propose a feature selection algorithm to reduce extracted features from network flows. The selected features are lately analyzed using a supervised machine learning technique to effectively detect the presence of botnets. The experimental evaluation based on a versatile existing data set shows that the proposed model is able to effectively detect bots with more accuracy and high detection rate with moderate false alarms in the botnet’s Command and Control (C&C) phase.

A wide community of sensor nodes is subscribing to the Mobile AdHoc network (MANET) and has self-directed commands. Individual nodes within the network can escort or without permission or prior warning. Therefore the MANET becomes widely applied in vital domains due to its dynamic and autonomous feature and its ease of installation. However, MANET is a target for malicious attacks. One of these attacks is coming from what is called black hole and gray holes nodes. Much published works addressed the detection mechanism developed for black holes. However, not a lot of attention was given to the analytic comparison with the gray hole. This work introduces, compares, and analyzes the most recent researches that addressed the development of gray holes as well as black holes identification tools. In this paper, we presented a comparative table of the most relevant papers, with an indication of the benefits and gaps for each of the protocols used and the approach or technique implemented by the researchers. Then, we presented a summary of the best papers that received a high rating. And presented subsequently our vision and recommendations to inspire and motivate researchers to work on these observations in the future.

Electromyography (EMG) is a sensor to diagnose the health of muscle with the placement of electrodes through the surface of the skin. Nowadays, muscular disorder occurs where the problem is the cause of muscle weakness, pain, fatigue, and also due to paralysis. Biomedical applications where the electromyography (EMG) sensor is used to collect data of a normal muscles contradiction, and there are two classifiers which are linear discriminant analysis (LDA) and support vector machines (SVM) that are prompt to undergo with the process and also mean absolute value feature (MAV) traces are used to extract from the electromyography (EMG). Ascertain part of body muscle targeted with the different subject with certain body mass index (BMI), the movement contradiction of the muscles through the sensor pad electrodes that are attached to the human body where the point of muscles that wanted to observe and the data collected or being used. The study provides guidelines for the prediction of muscle fatigue using electromyography (EMG) sensors with classification methods that are approved.

Nowadays, many advanced countries are taking the advantage of the modern robotic technology to develop and deploy robots in hospitals, airports and healthcare centers to assist the nation combat the deadly virus, known as COVID-19. This Research paper focuses on an artificial intelligence based robotic device that is developed. The robot is equipped with germs and virus killing Ultraviolet (UV) light system along with conventional spraying system for cleaning off any possibly infected area. The robot titled as "Assistive Robot for Covid-19 (ARC-19)" functions on two modes of operations, Autonomous and Semi-Autonomous. Traditionally, the cleaning job is done by cleaning workers, and the healthcare staff have direct contact with the COVID-19 patient. Instead, the developed contactless ARC-19 system can easily disinfect any possibly infected high-touch surface, and reduce the direct-contact between the patient and healthcare staff. After testing the robot, it turns out that the robot was able to kill 98% of the bacteria and other harmful viruses including COVID-19. This could lead to cost-effective, fast, and practical method to reduce the risk on being infected by COVID-19.

To protect under-represented and marginalized workers from the upcoming automation boom, it is imperative to critically examine the past technological breakthroughs and their impacts on workers. In the last two decades of the twentieth century, computer-aided newspaper production technology gradually started becoming popular in Bangladesh, making the traditional hot metal typesetting technology obsolete. The typesetters, who relied on manual labor and a very basic level of literacy, experienced mass layoffs and had to adapt to new technology to survive in their profession. In this work, we interviewed 17 typesetters from that period to understand their technology adoption dynamics. Our findings reveal that despite a few success stories, most of the typesetters failed to adopt and gradually went into oblivion, mainly because of local infrastructural issues. We connect our findings to broader issues of culture, politics, and economics and provide concrete suggestions and policy recommendations to protect more than six million workers who are vulnerable to technology-induced job displacement in the next 20 years in Bangladesh.

Forest soils are often a sink for atmospheric methane (CH4) and are thus worth special attention in the context of mitigation of greenhouse gases (GHGs) and offset of agricultural GHG emissions at farm to national levels. The litter layer influences the exchange of GHGs between soil and atmosphere; however, most studies focus on the contribution of only soil to the CH4 cycle. In order to improve the inventory of this gas, it is worth investigating how litter influences the exchange of GHGs. Its effect on CH4 uptake may vary in deciduous and coniferous sites due to the different properties of litter. Field experiments were carried out to assess the CH4 uptake capability in 5 different soil types (with and without litter) under different forest types (deciduous, coniferous, and mixed) in Poland. During summer 2019, the highest CH4 uptake (about 2 mg C m-2 day-1) in a variant without litter on the ground was detected in Dystric Cambisol (with the highest C/N ratio) under a 100-year-old coniferous forest and in Albic Luvisol under a 58-year-old mixed forest. The presence of the litter level reduced the CH4 flux in the range of 6-27% in these locations. Methane consumption was the lowest in silty soils (~ 0.4 – 1 mg C m-2 day-1) in the mixed forest and decreased by 13-29% when covered with the litter layer. The negative effect of the litter layer on CH4 absorption was the lowest (~ 3-4%) in sandy Eutric Gleysol under a 75-year-old deciduous forest with 90% of oak and 10% of European hornbeam. The dry conditions in the summer 2019 (with total rainfall 163 mm during the tested months in the studied region) resulted in low moisture in both the litter and soil. However, even low-humidity litter (below 10%) reduced CH4 consumption rates in the measured sites.Research was partially conducted under the project financed by Polish National Centre for Research and Development within of ERA-NET CO-FUND ERA-GAS Programme (ERA-GAS/I/GHG-MANAGE/01/2018).

It has been widely reported that although IPCC methodologies appropriate for national-level accounting purposes, they lack the farm level resolution and holistic approach required for whole-farm systems analysis. The importance of evaluating greenhouse gas (GHG) emissions from crop production, animal farming and agroforestry within the whole farm setting is being realized as more important than evaluating these emissions in isolation. Thus, whole-farm systems modelling is widely used for farm-level analysis. Here we compare three whole-farm models e.g. FarmSim, Holos and IFSM to simulate the effect of management practices on GHG emissions at the whole farm level and evaluate the carbon sequestration and methane oxidation potential of afforestation as a compensation mechanism for the mitigation of farm-level GHG emissions. Ideally, we would also want information on model performance in predicting GHG emissions in future climatic scenarios. Initial results indicate that these models can accurately predict CO2 emissions but the accuracy of these models for predicting methane (CH4) and nitrous oxide (N2O) emissions is quite low. We found that the most prominent drivers for GHG emissions in a whole farm setting were the enteric CH4 from animal farming and N2O emissions from soil management in cropland. Thus, the low prediction accuracy for CH4 and N2O emissions in whole-farm models may introduce substantial errors into GHG inventories and lead to incorrect mitigation recommendations, which necessitates further fine-tuning of these models. Efforts are ongoing to integrate carbon sequestration and soil methane oxidation potential of farm-level afforestation in the whole farm models. There are indications that afforestation can be an effective mitigation strategy. The variation we found in farm system parameters, and the inherent uncertainties associated with emissions of CH4 and N2O can have substantial implications for reported agricultural emissions requiring uncertainty or sensitivity analysis in any modelling approach. Although there is considerable variation among the quality of farm data, boundary assumptions, the emission factors used we suggest that whole-farm systems models are an appropriate tool to develop and measure GHG mitigation strategies for the European farmed landscape.

Birth defects or several tragic events can result in the loss of a hand that can be physically and emotionally incredibly difficult to bear. In the study, a low-cost, prosthetic hand has been developed and managed, irrespective of geographical and financial conditions. It would be feasible and affordable to many people to create a prosthetic hand with basic materials, available in most areas and powered by servomotors, based on muscle motion detected by the use of Resistive Force Sensor (RFS). Due to its simplicity and low cost, the Arduino controller is used as a main control unit for the operation of this prosthesic. It is shown that throughout the experimental work it has been figured out that a developed low-cost prosthetic hand is working well and affordable. The findings of experiments indicate that the success rate for choosing big and light weight items is about 78%. But because of the simple lightweight materials used in constructing it, it cannot withstand large and small items very well.

In recent decades diversified strategies have been applied for the prediction of flash floods more accurately as it is one of the most dangerous natural disaster in the world. Flash floods are usually very rapid and sudden therefore forecasting the exact time of flood and venue can be acknowledged as a complex task. In the recent era Machine learning and Artificial Intelligence based predictive models have been developed for the robust and vigorous forecasting of floods. Data set from the different appropriate sensors can be collected and processed through any predictive algorithm. Prediction can be based on the historical data as well therefore in this research paper the data has been collected from the Pakistan Meteorological Department (PMD) for the duration of 2016 to 2017. The data can be categorized into three levels of danger. Level 3 is the lowest level, Level 2 was set as the moderate level and Level 3 as a dangerous level. These levels must be announced and communicated to the people of the vicinity of the affected area for the evacuation routes. Robust classification of these levels are mandatory in order to classify the true event therefore very popular classifiers named as support vector machine and K-NN classifiers have been applied on the data set for the classification of levels accurately and precisely. Simulated results on the filtered data proved that Support vector machine (SVM) performed better classification compared to the K-NN algorithm in terms of efficiency, confusion matrix, precision, recall and fl-score and prediction speed.

Recent advancements in the field of rehabilitation robotics have led to the development of variety of exoskeletons that are used extensively during therapeutic exercises for the poststroke patients. These robotic devices are meant to provide ease and comfort to the patient undergoing therapy, so controlling these robots effectively is very crucial. Widely used control algorithms for controlling these devices are linear controllers which are not much robust and efficient as compared to non-linear controllers. Since rehabilitation robots are highly nonlinear in nature, therefore nonlinear controllers are required that can perform well under uncertainties and resistant to parametric changes. One of the most extensively used non-linear control method is Sliding Mode Controller (SMC). SMC has proved its robustness in terms of trajectory tracking, stability and insensitiveness to disturbances and parametric uncertainties. One drawback of using classical SMC is chattering, which is high frequency oscillations. This paper presents sliding mode controller and its different types along with the method of chattering removal, for controlling rehabilitation robotic devices.

Smart meter is an electronic device that measures the energy consumption by different time intervals, this time interval may be in minutes, hours, days or months. The proposed technology collects the data from meter and transfer that data to central database for storing data, analyzing and also monitoring the safety related issues. Moreover, it also includes the module which has provision of on demand units loading facility from the energy provider company by just sending a request SMS. The proposed design has been first implemented in software and then a prototype model has been developed, which communicates with the energy service provider for the consumed energy data through GSM network. The purpose of this paper is to present theory and implementation related to remote monitoring and controlling of the energy meter by using GSM architecture. This GSM based system enables the electricity department to get the meter readings regularly while avoiding manual meter reading. The work also enables prepaid billing, electricity management, energy saving and implements a mechanism for electricity theft detection.

Religious-spiritual (R/S) education helps medical students cope with caregiving stress and gain skills in interpersonal empathy needed for clinical care. Such R/S education has been introduced into K-12 and college curricula in some developed nations and has been found to positively impact student's mental health. Such a move has not yet been seen in the Indian education system. This paper aimed to examine perspectives of teachers and parents in India on appropriateness, benefits, and challenges of including R/S education into the school curriculum and also to gather their impressions on how a R/S curriculum might promote students' health. A cross-sectional study of religiously stratified sample of teachers and parents was initiated in three preselected schools in India and the required sample size (N = 300) was reached through snowballing technique. A semi-structured questionnaire, with questions crafted from "Religion and Spirituality in Medicine, Physicians Perspective" (RSMPP) and "American Academy of Religion's (AAR) Guidelines for Religious Literacy," was used to determine participants' perspectives. Findings revealed that teachers' and parents' "comfort in integrating R/S into school curriculum" was associated with their gender (OR 1.68), education status (OR 1.05), and intrinsic religiosity (OR 1.05). Intrinsic religiosity was significantly (p = 0.025) high among parents while "intrinsic spirituality" was high (p = 0.020) among teachers. How participants' R/S characteristics influence their support of R/S education in school is discussed. In conclusion, participants believe R/S education will fosters students' emotional health and interpersonal skills needed for social leadership. A curriculum that incorporates R/S education, which is based on AAR guidelines and clinically validated interpersonal spiritual care tools would be acceptable to both teachers and parents.

Background: Dyslipidemia is one of the modifiable risk factors of coronary heart disease. Objective: To determine the pattern of dyslipidemia among rural population. Methodology: This was a cross sectional study conducted in rural area of Rahim Yar Khan as a part of large study regarding prevalence of coronary heart disease. A total of 2000 study subjects were included by systematic random sampling technique, from 22 clusters of subdistricts, Rahim Yar khan, Pakistan. Patients excluded from study were; on statin therapy, patients with hypothyroidism, diabetes mellitus. Informed verbal consent was taken form each patient. Fasting venous blood samples was taken for serum total cholesterol level, serum triglycerides level, low density lipoprotein (LDL) and high-density lipoprotein (HDL). Lipid profile was considered deranged if any one of the followings was present: Total Cholesterol level >200mg/dl, Triglyceride (TG) level >150mg/dl, High density lipoprotein (HDL) 130 mg/dl. Data was entered and analyzed using computer program SPSS version 16.0. Results: A total of 2000 study subjects were included in this study, with male 792(39.6%) and females 1208 (60.4%). Mean age of study subjects was 46±9.6 years. Overall 843 (42%) study subjects have deranged lipids, with 26 (1.3%) having high LDLc, 45 (2.2%) having high cholesterol, 375 (18.8%) having high triglycerides and 538 (27%) low HDLc. Conclusion: This study showed that there was high frequency of deranged lipids among general population of rural background. Most common component among deranged lipids were low HDLc, followed by high triglycerides, high cholesterol and high LDLc. Key Words: Dyslipidemia, Rural population, Prevalence

Solar ultraviolet (UV) radiation is the main factor of photocarcinogenesis, photoaging, and photosensitivity; thus protection from biological damaging UV radiation is a concern. Sunscreens containing UV filters are the most preferred means of photoprotection but the safety and efficacy of UV filters are in question. Benzophenone (BP) and its derivatives, namely, benzophenone 1 (BP1), is commonly used in sunscreens as a UV blocker. The aim of this study was to assess the effects of BP and BP1 on the differential expression of proteins in human keratinocytes (HaCaT cells) under exposure to ultraviolet A radiation. Photosensitive proteins were screened from HaCaT cells by two-dimensional (2-D) gel electrophoresis, and identification of these differentially expressed proteins was performed by matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF)/TOF mass spectrometry. Protein identification was performed using the search program MASCOT and a database made of SUMO and GhJMJ12 amino acid sequences. Our results showed that the proteins involved directly or indirectly in apoptosis are 70 kDa heat shock protein, long-chain specific acyl-CoA dehydrogenase, serine/threonine-protein kinase, and FAM78A protein, which were upregulated in comparison to control HaCaT cells. The expressions of binding immunoglobulin protein, podocalyxin-like protein, actin, cytoplasmic, and calreticulin precursors were downregulated. The altered protein expression indicated that cell growth arrest and apoptosis were potential mechanisms of cytotoxicity and genotoxicity of BPs. The results of 2-D gel electrophoresis followed by mass spectrometry showed expression of novel proteins involved in promoting or initiating apoptotic pathways. Hence, we conclude that BPs should be avoided as a UV blocker from sunscreens because of its potential to promote apoptotic proteins in human skin keratinocytes.

Brain computer interface (BCI) technology can be used to design a robotic arm whose decision would be based on the brain activity and brain signals. This proposed design can be more beneficial for the paralyzed people and the patients who are suffering from Amyotrophic lateral sclerosis (ALS), Locked in syndrome (LIS), or neurodegenerative disease. Due to these disease patients would not be able to hold and grip the objects properly. Extensive literature review showed that various EEG signal analysis has been completed with the accuracy of 70% to 85%. The suggested solution would be beneficial to the patients in terms of performing every day functions easily like draws opening, holding dishes and opening and closing of doors as well with more accuracy. In the proposed research electroencephalogram signals were observed and used to classify the type of the motion. Data acquisition comprised of three stages amplification can be considered as cost effective signal conditioning. High pass filter, low pass filter and then converted from analog to digital. Open vibe software was used to design the basic neuron scenario for the brain signals and then classified into alpha and beta waves. Robotic arm movement was based on the alpha and beta waves were performed precisely. Simulated results proved that proposed EEG signals acquisition performed better and can be acknowledged as cost effective. Researchers showed the successful execution of the brain wave signal classification with less false alarm rate for the robotic arm movement by modulation, digitization of the brain signal. Moreover, comparative analysis has been performed of Quadratic Discriminant analysis, k-NN and Medium Gaussian SVM in terms of accuracy prediction speed and training time. Comparative analysis proved that Medium Gaussian SVM worked better than the other classifiers with the accuracy of 95.8%. It was also proved that Medium Gaussian classifier has the capability to predict 10000 observations per second in 0.75466 training time.

In this paper, we propose an Ultra-wideband Balanced antipodal Vivaldi antenna embedded with equal rectangular shaped fins (RF-BAVA) for wide-band imaging application has been introduced. Further, dielectric substrate which elliptical in shaped with low permittivity constant value has been used in the extension of array antenna in H-plane in order to enhance the radiation characteristic of an antenna(RFBAVA-D). The proposed single element of an antenna designed on special purpose dielectric-material Rogers RT/Duroid 5880 with thickness of l.57mm and relative permittivity of $\varepsilon_{\mathrm{r}}=2.2$ by including 15 slits of same dimension on both arms of an antenna. The dimension of each element is 60.75mm x 66mm approximately. Particularly in the case of microwave imaging and its based application a stability of high gain is considered as an important requirement. So, the H-plane array-antenna principal approach has been adopted to improve antenna gain and polarization performance of array antenna based imaging system. The elements in H-plane almost cover entire ultra-wideband (3GHz-to-l0.5GHz) frequency range with the reflection coefficient $\mathrm{S}_{11}\lt-10\mathrm{d}\mathrm{B}$. Based on the simulation results, the array elements with extended dielectric-director in H-plane arrangement offering high-gain up to 13dB with stable radiation pattern and good impedance bandwidth frequency on all ports while the single antenna element produces around lldB. A targeted stable gain with low side-lobe level has been achieved in H-plane configuration with better directivity instead of single antenna element. The design and parametric evaluation of RF-BAVA-D has been verified using CST simulation software.

Internet of thing (IoT) is a new concept where the term ‘thing’ is associated with the configurable sensors and devices no matter domestic or industrial, whereas bridging up a relationship in between these things and internet protocol is known as Internet of thing. Moreover, the same concept has been introduced in the field of robotics as ‘Internet of Robotic Things (IoRT)’, which is mainly concerned with active sensorization of sensors dully interfaced with any type of robots i.e. autonomous unmanned ground vehicle (UGV). This paper describes the prototyping of an autonomous sewerbot that will not only identify the sewer defects in sewerage pipelines but will also identify the type of blockages using the technique of digital image processing. Furthermore, the deployed configurable sensors will also share the attributes of particular sewerage line on IoT such that temperature, humidity, availability of hazardous gases, exact depth at which it is available and global positioning using GPS module. The paper also provides the brief construction of this mechatronic and amphibian system via which it can extricate the blockages from sewerage lines along with wireless camera surveillance.

3D printing as a manufacturing method is gaining more popularity since 3D printing machines are becoming easily accessible. Especially in a prototyping process of a machine, they can be used, and complex parts with high quality surface finish can be manufactured in a timely manner. However, there is a need to study the effects of different manufacturing parameters on the materials properties of the finished parts. Specifically, this chapter explains the effects of six different process parameters on the impact resistance. In particular, print temperature, print speed, infill ratio, infill pattern, layer height, and print orientation parameters were studied, and their effects on impact resistance were measured experimentally. Moreover, the optimum values of the process parameters for impact resistance were found. This chapter provides an important guideline for 3D manufacturing in terms of impact resistance of the printed parts. Furthermore, by using this methodology the effects of different 3D printing process parameters on the other material, properties can be determined.

Demands for rehabilitation robots are now increasing day by day due to increase in the number of patients with neural disorder. These robots help the patients in therapeutic exercise performing specific movements which leads to mitigating neural disorders through a gradual improvement of the patients’ limb performances. As robots are the best suitable options to perform repetitive tasks without the risks of monotony and fatigue failure, rehabilitation via robots have proven to be more of a comfortable exercise than an exhausting treatment procedure. Rehabilitation robots require precise and efficient control in terms of position and force, ensuring thus accuracy in exercise movements. Nonlinear controllers make good option to this end as they adapt to handle the system uncertainties. This paper presents a Model Predictive Control (MPC) to control the rehabilitation robot for upper limb extremity under various disturbed conditions. From the results MPC proves to be robust controller under the action of applied external disturbances.

When the 3D printing process is considered, there are also other parameters, such as nozzle size, flow rate of material, print-speed, print-bed temperature, cooling rate, and pattern of printing. There are also dependencies that will be addressed in between these parameters; for example, if the printing temperature is increased, it is not clear if the viscosity of the material will increase or decrease. This chapter aims to explain the effect of printing temperature on layer sticking while dimensional accuracy is achieved. Theoretical modelling and experimental testing will be performed to prove the relationship. This type of formulation can be later adapted into a slicer program, so that the program automatically selects some of the printing parameters to achieve desired dimensional accuracy and layer sticking.

Aging of the skin is a gradual process, which is associated with changes in the appearance, characteristics, and function of the skin. Skin aging occurs through genetic, lifestyle, dietary, and environmental factors. Within the skin the production of collagen and elastin slows down, dead skin cells do not shed quickly, and the turnover of new skin cells decreases between the age group of 20 and 50. In addition to natural aging premature aging can also result which is due to sunlight exposure, chemical exposure, or other environmental pollutions. Ultraviolet radiation in sunlight damages the elastin and collagen fibers in the skin, which ultimately results in wrinkling of the skin at early stages of life. UV light induces approximately 99% of non-melanoma and 95% of melanoma-type skin cancers in humans. Excessive UV exposure produces genetic mutation that can lead to skin cancer. Skin carcinogenesis by DNA damage is considered a predominant paradigm for UV toxicity. Exposure to UV radiation can activate many oncogenes, which leads to skin cancer. Initiation and progression of skin carcinogenesis mediated by UV radiation involve complex pathways including those of apoptosis, proliferation, autophagy, DNA repair, metabolism, and inflammation. PTEN (phosphatase tensin homolog) is well established as a tumor suppressor gene that induces apoptosis and reduces cell proliferation by inhibition of the P13K/AKT pathway. p53 gene present in humans is being positioned on chromosome number 17 and mutations analyzed in the tumor suppressor gene p53 are one of prior genetic events which lead to the development of cancer in the skin due to exposure to UV rays.

Ofloxacin (OFLX) is a broad spectrum antibiotic, which generates photo-products under sunlight exposure. Previous studies have failed to explain the attenuated anti-bacterial activity of OFLX. The study was extended to explore the unknown molecular mechanism of photogenotoxicity on human skin cell line (HaCaT) under environmental UV-B irradiation. Photochemically OFLX generates ROS and caused 2′-dGuO photodegradation. We have addressed the binding affinity of OFLX and its photo-products against DNA gyrase. Significant free radical generation such as 1 O 2 , O 2 •− and • OH reduces antioxidants and demonstrated the ROS mediated OFLX phototoxicity. However, the formation of micronuclei and CPDs showed photogenotoxic potential of OFLX. OFLX induced cell cycle arrest in sub-G1 peak. OFLX triggers apoptosis via permeabilization of mitochondrial membrane with the downregulation of anti-apoptotic Bcl-2 and caspase-3 whereas, upregulation of pro-apoptotic Bax and Cyto- C proteins. Our study illustrated that binding affinity of OFLX photo-products with DNA gyrase was mainly responsible for the attenuated antimicrobial activity. It was proved through molecular docking study. Thus, study suggests that sunlight exposure should avoid by drug users especially during peak hours for their safety from photosensitivity. Clinicians may guide patients regarding the safer use of photosensitive drugs during treatment.

Rice production systems are the largest anthropogenic wetlands on earth and feed more than half of the world's population. However, they are also a major source of global anthropogenic greenhouse gas (GHG) emissions. Several agronomic strategies have been proposed to improve water-use efficiency and reduce GHG emissions. The aim of this study was to evaluate the impact of water-saving irrigation (alternate wetting and drying (AWD) vs. soil water potential (SWP)), contrasting land establishment (puddling vs. reduced tillage) and fertiliser application methods (broadcast vs. liquid fertilisation) on water-use efficiency, GHG emissions and rice yield. The experiment was laid out in a randomised complete block design with eight treatments (all combinations of the three factors) and four replicates. AWD combined with broadcasting fertilisation was superior to SWP in terms of maintaining yield. However, seasonal nitrous oxide (N2O) emissions were significantly reduced by 64% and 66% in the Broadcast-SWP and Liquid fertiliser-SWP treatments, respectively, compared to corresponding treatments in AWD. The SWP also significantly reduced seasonal methane (CH4) emissions by 34 and 30% in the broadcast and liquid fertilisation treatments, respectively. Area-scaled GWPs were reduced by 48% and 54% in Broadcast-SWP and Liquid fertiliser-SWP treatments respectively compared to the corresponding treatments in AWD. Compared to AWD, the broadcast and liquid fertilisation in SWP irrigation treatments reduced yield-scaled GWPs by 46% and 37%, respectively. In terms of suitability, based on yield-scaled GWPs, the treatments can be ordered as follows: Broadcast-SWP < Broadcast-AWD = Liquid fertiliser-SWP < Liquid fertiliser-AWD. Growing-season water use was 15% lower in the SWP treatments compared with the water-saving AWD. Reduced tillage reduced additional water use during land preparation. The conclusions of this study are that improved water management and timely coordination of N fertiliser with crop demand can reduce water use, N loss via N2O emissions, and CH4 emissions.

Demands for rehabilitation robots are now increasing day by day due to increase in the number of patients with neural disorder. These robots help the patients in therapeutic exercise performing specific movements which leads to mitigating neural disorders through a gradual improvement of the patients' limb performances. As robots are the best suitable options to perform repetitive tasks without the risks of monotony and fatigue failure, rehabilitation via robots have proven to be more of a comfortable exercise than an exhausting treatment procedure. Rehabilitation robots require precise and efficient control in terms of position and force, ensuring thus accuracy in exercise movements, ensuring with element of enjoyment patients' safety. Nonlinear controllers make good option to this end as they adapt to handling the system uncertainties and parametric changes. This paper presents a Model Predictive Control (MPC) to control the rehabilitation robot for upper limb extremity under disturbed conditions. From results maximum overshoot of 1.4 and 1.0 and steady state error of 0.99 is found under disturbed and noisy condition respectively. Hence MPC proves to be a robust controller of external disturbances rejection and noise filtration.

Responses and durability of energy independent neutron dosimeters based on proton detector microchips covered with segmented multi-thickness polythene (PE) layers are badly affected by the slow neutrons produced in the process. Here, we report the shielding of slow neutrons by using 0.9 cm thick cadmium layer, which is transparent to fast neutrons. This is achieved by using 15 MeV beam of 107 number of neutrons incident on poly methyl methacrylate-aluminum-cadmium (PMMA-Al-Cd) multiple layers by means of a Monte Carlo method. The results can be used in real time dosimeters for measuring the tissue equivalent neutron doses, and for optimizing the shielding of thermal neutrons in the vicinity of proton therapy centers.

One of the biggest challenges faced by technology based Small and Medium Enterprises SME companies in Malaysia is to prototype their casing or housing for their customized electronic products such as embedded control systems, power supplies etc. One of the SME company from Malaysia involved in this case study also have faced the similar problem for their upcoming R&D products. Since the emerging of 3D printer’s technology, rapid prototyping is becoming more superior for their fast, cheap, and reliable compared to conventional or traditional manufacturing technology. The SME company used this as opportunity and used it for their prototype products and have saved time up to 75% and cost by 72% by decreasing the weight of the casing up to 77% using 3D printers.

Flash floods are very abrupt and sudden and can devastate large areas with in fraction of seconds. Tsunami is also a grass root cause of infrastructure loss and casualties. Tsunami is caused by the release of energy inside the ocean. It has been observed that Tornados and Tsunami frequently occurred that caused more than 120000 casualties between 1992 to 2005. Several approaches and cases have been studied and applied to overcome this topmost issue. Strong and reliable flood risk management system must have the capabilities to forecast and estimate the change in the process: the hydro atmospheric and climatic development that causes flood waves and land and metropolitan exposure that leads to casualties. Techniques utilized for the early investigation of flash floods may be classified into following categories a. Sensors and instrumentation-based b. Radar based c. Satellite images based. In this paper researchers presented a reliable and vigor cost-effective solution for the prediction of flash floods accurately and precisely by using direct measurements with the combination of scaled conjugate gradient back propagation. Researchers have adopted direct measurement method by utilizing couple of sensors named as PIR, ultrasonic sensor, water or humidity sensor, temperature and pressure sensors. Gas sensor has also been used to detect the increased carbon dioxide levels in the environment as soil is saturated more and the probability of flash flood occurrence becomes sure in this situation. An appropriate mixture of estimation sensors can extensively boost the benefit of information in contrast with that from a single sensor. 48 hours of data has been recorded and processed for the false alarm identification by using Scaled conjugate gradient back propagation. Results proved that our proposed system worked better or equivalent to the other available techniques with minimized error rate. Results showed that it performed better than the existing approaches. Multi-layer perceptron (MLP) was applied on the same data set for the error and false alarm classification. Results were compared with the multi-layer perceptron and it can be easily observed that they are close enough therefore scaled conjugate gradient back propagation performed very well.

Proportional Integral Derivative (PID) controller is most widely used feedback controller used in various applications such as control systems, instrumentation and motor drives. Tuning of PID parameters is challenging to researchers, despite of its popularity. This paper presents the optimization of PID controller using particle swarm optimization for controlling upper limb rehabilitation robot. The proposed algorithm offers easy implementation, less computation burden and fast convergence of the system response. PSO is an evolutionary algorithm which is based on the social behavior of fish and flocking of birds. This study will demonstrate in brief how to employ Particle swarm optimization on PID. MATLAB simulations have been carried out and graphs have been plotted which shows less percent overshoot and a significant rise time of the system having no steady state error. The results proved that PSO-PID is stable and efficient control algorithm for upper limb rehabilitation robot.

Collecting large amount of data are made easier and simpler by taking advantage of Wireless Sensor Network and Internet of Thing technology. Rampant use of these technology in today's time have made it inexpensive and cost effective to be applied as research tools. The action of saving data over long period of time can be done flawlessly by maximizing the big capacity of cloud storage. This paper presents the application of WSN and IoT technology to monitor and record data of real time current usage for split unit air conditioners placed in classroom. The classroom uses two air conditioners with 4HP to control its ambient temperature. Microcontroller is utilized to transmit the data collected by current clamp sensor to the cloud server over the internet.

Anticipating such catastrophic road accident is one of the main interest of Malaysian government policies, vehicle manufactures strategies, and research in automotive safety. The vital aspect that causes road accident is a human error due to the behavior of the drivers. This research presents approaches for alertness detection based on the electroencephalography and power spectrum to evaluate driver's vigilance level in a static driving simulator. Twenty datasets were acquired using the eight-channel wireless headset for EEG signals recording. Two driving tasks, that are Alert Driving (AD) and Fatigue Driving (FD), were instigated for data acquisition, to discriminate the driver's alertness level. The AD task is designed to demonstrate the alertness condition and FD task is for the fatigued state. The acquired EEG signals are preprocessed to eliminate the noise interference and segmented into equal-length frame samples. The frame samples (signals) are segregated into four frequency range signals (i.e. Delta, Theta, Alpha, and Beta) and used to select the distinctive attribute. To estimate the alertness level, four different frequency-domain power spectral density (PSD) feature extraction techniques were evaluated (specifically periodogram, Lomb-Scargle, multitaper, and Welch). Multilayer Neural Network (MLNN) is used to evaluate the performance of all the extracted features. Ten-fold cross-validation method was used for validating and testing the reliability of the classifier model. The highest average accuracy is obtained from PSD using Welch with 96.7% and 85.0% for training accuracy and testing accuracy respectively.

The standard electric motors have devoted themselves a large share of total energy consumption in the world due to high electric consumption. One of the alternative solution to reduce energy consumption in mechanical systems that have large numbers of motor like HVAC system is using high efficient electric motor. Application of high efficient motor saving in this study presents investigation of energy saving, energy saving cost, payback period, and emission reduction by using efficient motors replacement to standard motors of HVAC system in one hospital in Malaysia (UKMMC). It was found that a considerable energy saving values for different loads (50%, 75%, and 100%) could be achieved by replacing the high efficient motors to standard motors. During the one year, total energy savings have been measured for different loads as 122.5, 120.9, and 99 MWh respectively. Also, it has been estimated that amounts of energy saving cost for different loads are US$12,244, US$12,095, and US$9,893. The best size and amount of payback period, and energy saving, saving cost and emission reduction was found for 50% load.

The aim of this study is to design a reliable and stable control for upper limb rehabilitation robot for post stroke patents. Position control is a key to perform an effective rehabilitation robotic exercise. Rehabilitation robots are coupled with humans which offer ascent to human-robot interaction dynamics and other parametric uncertainties such as environmental contacts and sensory noise. Model based controllers are not easy to implement and are unable to provide precise control in human-exoskeleton interaction because above mentioned factors don't permit to account exact dynamic model. The efficient control system is required to attain the optimal control performances which takes care the system stability and accuracy. Fuzzy Logic (FL) control has ability and it can provide accurate position/force control with the presence of model uncertainties. This paper presents a comparison between a conventional PID controller and online tuning of PID controller based on fuzzy inference system for extension/flexion movements of shoulder joint.

Mobility Assistance Robot (MAR) is a stand stool or an electronic walker. This robot can make a wheelchair bound person to experience the sensation of standing and navigating while standing up. This is not just a wheelchair substitute but indeed an absolutely new and advanced platform of mobility which makes the life of a disabled person easier. It can be controlled by an android mobile phone and users bind or tie themselves into a standing position without any difficulty. This robot can also be controlled through Electromyography (i.e. muscle movement). Electromyography is basically a method of monitoring the motion of neutrons, which are then converted into analog electrical signals and further working is then carried out through these signals. MAR has the potential to carry (lift) the disabled person automatically. It provides a platform for disabled person to ride on it and move with a maximum speed of 10 km/h. The robotic vehicle can be moved in any desired direction using both android mobile application and muscle movement. The person on ride can get the feeling of roaming around like a normal human with legs. This robot is specifically designed to provide a platform for disabled person to live like normal humans. This Robot has many social benefits and caters the need of real world.

Cancer is one of the most fatal diseases next only to cardiovascular diseases spread all around the globe, and it is the third most fatal disease in India. Environmental factors such as chemicals, UV light, tobacco products, X-rays, viruses, and disturbance in oncogenes are the factors which induce mutations that are inheritable and result in cancer. PDT comprises of three essential components: photosensitizer (PS), light, and oxygen. Oxygen in the form of reactive oxygen species can be toxic and may lead to cell death via necrosis or apoptosis. PDT is a two-stage procedure. Administration of a light-sensitive PS is followed by irradiation of tumor loci with a light of appropriate wavelength. This chapter describes about oncogenes and role of photodynamic therapy in treatment of oncogenes.

PAHs or polycyclic aromatic hydrocarbons belong to group of environmental pollutants which come under human carcinogens. PAHs is a class which refers to an immensely colossal number carbon and hydrogen along with two or more fused aromatic rings. These PAHs produced from chemicals occur naturally in coal, crude oil, and gasoline. The physical properties of PAHs include flammable, solid crystals at room temperature, and tobacco leads to the engenderment of these deleterious chemicals. Their presence is also detected in cigarette smoke and motor vehicle emissions up to a certain levels. The exposure of PAHs to general population is in the form of breathing ambient and indoor air, by the consumption of contaminated aliment containing cigarette smoke, tobacco, and polluted air. Occupational exposure of PAHs may be reason for breathing quandaries, chest pain, and vexation coughing and also cause cancer.

Phototoxicity induced by drugs is a non-immunological event that refers to the development of rashes as a result of the combined effects of photosensitizer and ultraviolet or visible radiation. The combinatorial effect of both light and chemical is necessary to elicit phototoxicity. Exposure to either light or chemical alone is insufficient to induce an adverse effect; however, when photoactivation of the chemical occurs, the abnormal reaction may arise. Mostly, chemicals having peak absorption within the UVA spectrum (320–400 nm) cause drug-induced photosensitivity reactions, although occasionally wavelengths within UV-B or visible range also cause phototoxic reactions. Various drugs are available in the market, but the awareness about their adverse effect is very little. Phototoxicity is one of the adverse effects which people should be made aware of. The photobehaviour of few such drugs is being discussed here.

Cosmetics are chemical substances or products that are used to change the look or fragrance of the human beings. Mostly cosmetics are applied usually on the face and hair area. Sun acts as a natural source of different wavelengths that also contains ultraviolet (UV-A, UV-B, and UV-C) and many other radiations. Many ingredients present in cosmetics show absorption maxima (λmax) under visible light followed by ultraviolet radiation. Photosensitization of cosmetics results in the formation of reactive oxygen species (ROS), i.e., 1O2 and O2−, by the two different photosensitization mechanisms (type-I and type-II). The photosensitization of cosmetic products finally leads to ROS production and photoproduct formation. Oxidative stress mediated damage to biomolecules including DNA, formation of cyclobutane pyrimidine dimers (CPDs), etc. Various adverse effects of cosmetics are being reported in the research articles that relate to genotoxicity, mutagenicity, photosensitization, skin irritation, acute toxicity, percutaneous absorption, and eye irritation. Endogenous and exogenous antioxidants suppress cosmetic toxicity as well as cellular defense machinery (Keap1/Nrf2 and MAPK) pathways. Phototoxicity of cosmetics may lead to skin and ocular damage as well as immune suppression. India is among the tropical countries, where most of the activities are done by common man in the bright sunlight like agriculture, commerce, sports, etc. Thus, information is needed regarding the cosmetic photoproducts and its phototoxicity for total human safety.