A novel methodology for stabilization of silver nanoparticles on cotton, nylon and cotton/nylon fabrics using chitosan and triethyl orthoformate for enhanced and elongated antibacterial performance.

In the present study, the commercially available three different fabrics cotton, nylon and cotton/nylon were modified by chitosan and silver nanoparticles using a crosslinker triethyl orthoformate (TEOF). Resulted cotton­silver (Ag-Cs-Cot), nylon­silver (Ag-Cs-Nyl) and cotton-nylon silver (Ag-Cs-Cot-Nyl) fabrics showed significant anti-bacterial activity even after 50 washing cycles. Silver nanoparticles were prepared by reducing silver nitrate through sodium borohydride at 0 °C. In FTIR spectra the peak at near 1650 cm-1 confirmed that TEOF mediated attachment of chitosan with fabrics (due to C=N) and the stretching of secondary amine near the 3375 cm-1 indicated the silver attachment to the amine group of the chitosan. In Scanning Electron Microscope (SEM) images smooth surfaces of fabrics without any damage by modification process were observed. The antibacterial activity was Analyzed by agar diffusion and broth dilution assays against Escherichia coli and Staphylococcus aureus bacterial strains and results showed 90% bacterial inhibition against E. coli and 89% bacterial inhibition against S. aureus. For testing the antibacterial durability, the modified fabrics were washed with non-ionic detergent (10g/l) for 15 minutes under aggressive stirring (100 rpm) at room temperature. The modified fabrics retained antibacterial activity over the 50 washing cycles. Finally, the commercial potential of cotton-silver fabric was evaluated by stitching it with the socks of football players and interestingly results showed that the modified fabric on the socks showed more than 90% bacterial inhibition as compared to the plain fabric after 70 minutes of playing activity.

Performance evaluation of machine learning models on large dataset of android applications reviews.

With an ever-increasing number of mobile users, the development of mobile applications (apps) has become a potential market during the past decade. Billions of users download mobile apps for divergent use from Google Play Store, fulfill tasks and leave comments about their experience. Such reviews are replete with a variety of feedback that serves as a guide for the improvement of existing apps and intuition for novel mobile apps. However, application reviews are challenging and very broad to approach. Such reviews, when segregated into different classes guide the user in the selection of suitable apps. This study proposes a framework for analyzing the sentiment of reviews for apps of eight different categories like shopping, sports, casual, etc. A large dataset is scrapped comprising 251661 user reviews with the help of 'Regular Expression' and 'Beautiful Soup'. The framework follows the use of different machine learning models along with the term frequency-inverse document frequency (TF-IDF) for feature extraction. Extensive experiments are performed using preprocessing steps, as well as, the stats feature of app reviews to evaluate the performance of the models. Results indicate that combining the stats feature with TF-IDF shows better performance and the support vector machine obtains the highest accuracy. Experimental results can potentially be used by other researchers to select appropriate models for the analysis of app reviews. In addition, the provided dataset is large, diverse, and balanced with eight categories and 59 app reviews and provides the opportunity to analyze reviews using state-of-the-art approaches.

Sulforaphane and Its Protective Role in Prostate Cancer: A Mechanistic Approach.

The increasing incidence of prostate cancer worldwide has spurred research into novel therapeutics for its treatment and prevention. Sulforaphane, derived from broccoli and other members of the Brassica genus, is a phytochemical shown to have anticancer properties. Numerous studies have shown that sulforaphane prevents the development and progression of prostatic tumors. This review evaluates the most recent published reports on prevention of the progression of prostate cancer by sulforaphane in vitro, in vivo and in clinical settings. A detailed description of the proposed mechanisms of action of sulforaphane on prostatic cells is provided. Furthermore, we discuss the challenges, limitations and future prospects of using sulforaphane as a therapeutic agent in treatment of prostate cancer.

Nano-Selenium Antagonized Cadmium-Induced Liver Fibrosis in Chicken.

Cadmium is a global ecological toxic pollutant; in animals, hepatotoxic fibrosis is caused by bioaccumulation of Cd through food chains. We determined the path of nano-Se antagonism in Cd-induced hepatocyte pyroptosis by targeting the APJ-AMPK-PGC1α pathway, using an in vivo model of hepatotoxicity. All 1-day-old chicks were treated with Cd (140 mg/kg BW/day) and/or nano-Se (0.3 or 0.6 mg/kg BW/day) for 90 days. The result showed that Cd (1.55 ± 0.148) activated NLRP3 inflammasome 49.903% as compared to the Con group (1.034 ± 0.008) to release the inflammasome as a result of hepatocyte pyroptosis (2.824 ± 0.057). Compared with the Con group (1.010 ± 0.021), Kupffer cells were 219.109% more to activate astrocytes through the APJ-AMPK-PGC1α pathway, resulting in 185.149% more hepatic fibrosis. However, the fibrosis degree of the H-Se + Cd group (1.252 ± 0.056) was 56.5278% (p < 0.001) lower than that of the Cd group (2.880 ± 0.124). Therefore, this study established that pyroptotic hepatocytes and Kupffer cells could be targeted for nano-Se antagonizing Cd toxicity, which reveals a potential new approach targeting astrocytes for the treatment of liver fibrosis triggered by Cd pollution.

Novel approach for quantitative and qualitative authors research profiling using feature fusion and tree-based learning approach.

Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, etc., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself. Self-citation is important to evaluate an author's research profile and has gained popularity recently. Although different criteria are found in the literature regarding appropriate self-citation, self-citation does have a huge impact on a researcher's scientific profile. This study carries out two cases in this regard. In case 1, the qualitative aspect of the author's profile is analyzed using hand-crafted feature engineering techniques. The sentiments conveyed through citations are integral in assessing research quality, as they can signify appreciation, critique, or serve as a foundation for further research. Analyzing sentiments within in-text citations remains a formidable challenge, even with the utilization of automated sentiment annotations. For this purpose, this study employs machine learning models using term frequency (TF) and term frequency-inverse document frequency (TF-IDF). Random forest using TF with Synthetic Minority Oversampling Technique (SMOTE) achieved a 0.9727 score of accuracy. Case 2 deals with quantitative analysis and investigates direct and indirect self-citation. In this study, the top 2% of researchers in 2020 is considered as a baseline. For this purpose, the data of the top 25 Pakistani researchers are manually retrieved from this dataset, in addition to the citation information from the Web of Science (WoS). The self-citation is estimated using the proposed model and results are compared with those obtained from WoS. Experimental results show a substantial difference between the two, as the ratio of self-citation from the proposed approach is higher than WoS. It is observed that the citations from the WoS for authors are overstated. For a comprehensive evaluation of the researcher's profile, both direct and indirect self-citation must be included.

Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network.

A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce.

Analyses of Public Attention and Sentiments towards Different COVID-19 Vaccines Using Data Mining Techniques.

COVID-19 is a widely spread disease, and in order to overcome its spread, vaccination is necessary. Different vaccines are available in the market and people have different sentiments about different vaccines. This study aims to identify variations and explore temporal trends in the sentiments of tweets related to different COVID-19 vaccines (Covaxin, Moderna, Pfizer, and Sinopharm). We used the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool to analyze the public sentiments related to each vaccine separately and identify whether the sentiments are positive (compound ≥ 0.05), negative (compound ≤ −0.05), or neutral (−0.05 < compound < 0.05). Then, we analyzed tweets related to each vaccine further to find the time trends and geographical distribution of sentiments in different regions. According to our data, overall sentiments about each vaccine are neutral. Covaxin is associated with 28% positive sentiments and Moderna with 37% positive sentiments. In the temporal analysis, we found that tweets related to each vaccine increased in different time frames. Pfizer- and Sinopharm-related tweets increased in August 2021, whereas tweets related to Covaxin increased in July 2021. Geographically, the highest sentiment score (0.9682) is for Covaxin from India, while Moderna has the highest sentiment score (0.9638) from the USA. Overall, this study shows that public sentiments about COVID-19 vaccines have changed over time and geographically. The sentiment analysis can give insights into time trends that can help policymakers to develop their policies according to the requirements and enhance vaccination programs.

Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques.

The disease known as COVID-19 has turned into a pandemic and spread all over the world. The fourth industrial revolution known as Industry 4.0 includes digitization, the Internet of Things, and artificial intelligence. Industry 4.0 has the potential to fulfil customized requirements during the COVID-19 emergency crises. The development of a prediction framework can help health authorities to react appropriately and rapidly. Clinical imaging like X-rays and computed tomography (CT) can play a significant part in the early diagnosis of COVID-19 patients that will help with appropriate treatment. The X-ray images could help in developing an automated system for the rapid identification of COVID-19 patients. This study makes use of a deep convolutional neural network (CNN) to extract significant features and discriminate X-ray images of infected patients from non-infected ones. Multiple image processing techniques are used to extract a region of interest (ROI) from the entire X-ray image. The ImageDataGenerator class is used to overcome the small dataset size and generate ten thousand augmented images. The performance of the proposed approach has been compared with state-of-the-art VGG16, AlexNet, and InceptionV3 models. Results demonstrate that the proposed CNN model outperforms other baseline models with high accuracy values: 97.68% for two classes, 89.85% for three classes, and 84.76% for four classes. This system allows COVID-19 patients to be processed by an automated screening system with minimal human contact.

COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images.

COVID-19 pandemic is widely spreading over the entire world and has established significant community spread. Fostering a prediction system can help prepare the officials to respond properly and quickly. Medical imaging like X-ray and computed tomography (CT) can play an important role in the early prediction of COVID-19 patients that will help the timely treatment of the patients. The x-ray images from COVID-19 patients reveal the pneumonia infections that can be used to identify the patients of COVID-19. This study presents the use of Convolutional Neural Network (CNN) that extracts the features from chest x-ray images for the prediction. Three filters are applied to get the edges from the images that help to get the desired segmented target with the infected area of the x-ray. To cope with the smaller size of the training dataset, Keras' ImageDataGenerator class is used to generate ten thousand augmented images. Classification is performed with two, three, and four classes where the four-class problem has X-ray images from COVID-19, normal people, virus pneumonia, and bacterial pneumonia. Results demonstrate that the proposed CNN model can predict COVID-19 patients with high accuracy. It can help automate screening of the patients for COVID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff. The performance comparison of the proposed approach with VGG16 and AlexNet shows that classification results for two and four classes are competitive and identical for three-class classification.

A review of the endocrine disrupting effects of micro and nano plastic and their associated chemicals in mammals.

Over the years, the vaste expansion of plastic manufacturing has dramatically increased the environmental impact of microplastics [MPs] and nanoplastics [NPs], making them a threat to marine and terrestrial biota because they contain endocrine disrupting chemicals [EDCs] and other harmful compounds. MPs and NPs have deleteriouse impacts on mammalian endocrine components such as hypothalamus, pituitary, thyroid, adrenal, testes, and ovaries. MPs and NPs absorb and act as a transport medium for harmful chemicals such as bisphenols, phthalates, polybrominated diphenyl ether, polychlorinated biphenyl ether, organotin, perfluorinated compounds, dioxins, polycyclic aromatic hydrocarbons, organic contaminants, and heavy metals, which are commonly used as additives in plastic production. As the EDCs are not covalently bonded to plastics, they can easily leach into milk, water, and other liquids affecting the endocrine system of mammals upon exposure. The toxicity induced by MPs and NPs is size-dependent, as smaller particles have better absorption capacity and larger surface area, releasing more EDC and toxic chemicals. Various EDCs contained or carried by MPs and NPs share structural similarities with specific hormone receptors; hence they interfere with normal hormone receptors, altering the hormonal action of the endocrine glands. This review demonstrates size-dependent MPs' bioaccumulation, distribution, and translocation with potential hazards to the endocrine gland. We reviewed that MPs and NPs disrupt hypothalamic-pituitary axes, including the hypothalamic-pituitary-thyroid/adrenal/testicular/ovarian axis leading to oxidative stress, reproductive toxicity, neurotoxicity, cytotoxicity, developmental abnormalities, decreased sperm quality, and immunotoxicity. The direct consequences of MPs and NPs on the thyroid, testis, and ovaries are documented. Still, studies need to be carried out to identify the direct effects of MPs and NPs on the hypothalamus, pituitary, and adrenal glands.

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

Assessing the physico-chemical parameters and some metals of underground water and associated soil in the arid and semiarid regions of Tank District, Khyber Pakhtunkhwa, Pakistan.

Good-quality water and food are the basic needs of humans, plants, and animals. Polluted groundwater and soil directly and indirectly affect organisms, which is the main environmental concern. In the current study, standard protocols of atomic absorption spectrometry were adopted for the investigation of selected metals (lead, chromium, and iron) in the collected groundwater and soil samples. The Pearson correlation coefficient (r) applied to groundwater and soil samples shows a positive perfect correlation among water parameters (conductivity and total dissolved solids) in all three sources. In the hand pump samples between water table (WT) and water source depth (WSD), Pearson correlation coefficient (r) value was found (r = 0.87) while between EC and TDS, it was r = 1. Similarly, in the bore hole samples between WT and WSD (r = 0.66), EC and TDS (r = 1), EC and Cr (r = 0.70), and TDS and Cr (r = 0.70), which showed weaker correlation. In the tube well samples, correlation between EC and TDS was high (r = 1). The correlation coefficient (r) values of the soil parameters in the hand pump (soil) samples between Fe and Cr (r = 0.86), in bore hole samples between Fe and Cr (r = 0.77), in tube well samples between Fe and Cr (r = 0.69), while all the other parameter correlations were found lower (r = 0.60). Between electrical conductivity and total dissolved solids, high relation has been observed between them (r = 1). Overall, results showed that in most of the studied samples, contents of the target metals were found above the allowable limit set by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA).

Therapeutic advances in the topical treatment of cutaneous leishmaniasis: A review.

Cutaneous leishmaniasis has been endemic since decades. Millions of cases are reported worldwide specially in developing and underdeveloped countries. There are 2 major types of cutaneous leishmaniasis based on the causating species found in different regions of the world. These include New and Old World cutaneous leishmaniasis, which are self-healing, but if not treated, these may cause severe scars and many other complications like mucosal involvement. The conventional gold standard treatment for both types is mainly intralesional or parenteral administration of antimonial. Lately, a great deal of research has been done on development of topical treatment based on single agent or combination therapy. This review summarizes the current state of literature regarding therapeutic outcome of topical treatment against cutaneous leishmaniasis caused by different species in different regions.

Recent advances in the analysis of nanoparticle-protein coronas.

In spite of radical advances in nanobiotechnology, the clinical translation of nanoparticle (NP)-based agents is still a major challenge due to various physiological factors that influence their interactions with biological systems. Recent decade witnessed meticulous investigation on protein corona (PC) that is the first surrounds NPs once administered into the body. Formation of PC around NP surface exhibits resilient effects on their circulation, distribution, therapeutic activity, toxicity and other factors. Although enormous literature is available on the role of PC in altering pharmacokinetics and pharmacodynamics of NPs, understanding on its analytical characterization methods still remains shallow. Therefore, the current review summarizes the impact of PC on biological fate of NPs and stressing on analytical methods employed for studying the NP-PC.

Sentinel surveillance of selected veterinary and public health pathogens in camel population originating from Southern Punjab province, Pakistan.

An extended range of host susceptibility including camel has been evidenced for some of the important veterinary and public health pathogens, such as brucellosis, peste des petits ruminants (PPR) and bluetongue (BT). However, in disease endemic settings across many parts of the globe, most of the disease control interventions accounts for small and large ruminants, whereas unusual hosts and/or natural reservoirs, such as camels, remain neglected for disease control measures including routine vaccination. Such a policy drawback not only plays an important role in disease epizootiology particularly in settings where disease is endemic, but also serves an obstacle in disease control and subsequent eradication in future. With this background, using pre-validated ELISA and molecular assays [multiplex PCR, reverse transcriptase (RT)-PCR and real-time (rt)-PCR], we conducted a large-scale pathogen- and antibody-based surveillance for brucellosis, peste des petits ruminants and bluetongue in camel population (n = 992) originating from a wide geographical region in southern part of the Punjab province, Pakistan. Varying in each of the selected districts, the seroprevalence was found to be maximum for bluetongue [n = 697 (70.26%, 95% CI: 67.29-73.07)], followed by PPR [n = 193 (19.46%, 95% CI: 17.07-22.09)] and brucellosis [n = 66 (6.65%, 95% CI: 5.22-8.43)]. Odds of seroprevalence were more significantly associated with pregnancy status (non-pregnant, OR = 2.23, 95% CI: 1.86-5.63, p<0.01), farming system (mixed-animal, OR = 2.59, 95% CI: 1.56-4.29, p<0.01), breed (Desi, OR = 1.97, 95% CI: 1.28-4.03, p<0.01) and farmer education (illiterate, OR = 3.17, 95% CI: 1.45-6.93, p<0.01) for BTV, body condition (normal, OR = 3.54, 95% CI: 1.92-6.54, p<0.01) and breed (Desi, OR = 2.19, 95% CI: 1.09-4.40, p<0.01) for brucellosis, and feeding system for PPR (grazing, OR = 2.75, 95% CI: 1.79-4.22, p<0.01). Among the total herds included (n = 74), genome corresponding to BT virus (BTV) and brucellosis was detected in 14 (18.92%, 95 CI: 11.09-30.04) and 19 herds (25.68%, 95% CI: 16.54-37.38), respectively. None of the herds was detected with genome of PPR virus (PPRV). Among the positive herds, serotype 1, 8 and 11 were detected for BTV while all the herds were exclusively positive to B. abortus. Taken together, the study highlights the role of potential disease reservoirs in the persistence and transmission of selected diseases in their susceptible hosts and, therefore, urges necessary interventions (e.g., inclusion of camels for vaccine etc.) for the control of diseases from their endemic setting worldwide.

Heartbeat Sound Signal Classification Using Deep Learning.

Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.

A comparative study of bacterial diversity based on culturable and culture-independent techniques in the rhizosphere of maize (Zea mays L.).

OBJECTIVE: Maize is an important crop for fodder, food and feed industry. The present study explores the plant-microbe interactions as alternative eco-friendly sustainable strategies to enhance the crop yield. METHODOLOGY: Bacterial diversity was studied in the rhizosphere of maize by culture-dependent and culture-independent techniques by soil sampling, extraction of DNA, amplification of gene of interest, cloning of desired fragment and library construction. RESULTS: Culturable bacteria were identified as Achromobacter, Agrobacterium, Azospirillum, Bacillus, Brevibacillus, Bosea, Enterobacter, Microbacterium, Pseudomonas, Rhodococcus, Stenotrophomonas and Xanthomonas genera. For culture-independent approach, clone library of 16S ribosomal RNA gene was assembled and 100 randomly selected clones were sequenced. Majority of the sequences were related to Firmicutes (17%), Acidobacteria (16%), Actinobacteria (17%), Alpha-Proteobacteria (7%), Delta-proteobacteria (4.2%) and Gemmatimonadetes (4.2%) However, some of the sequences (30%) were novel that showed no homologies to phyla of cultured bacteria in the database. Diversity of diazotrophic bacteria in the rhizosphere investigated by analysis of PCR-amplified nifH gene sequence that revealed abundance of sequences belonging to genera Azoarcus (25%), Aeromonas (10%), Pseudomonas (10%). The diazotrophic genera Azotobacter, Agrobacterium and Zoogloea related nifH sequences were also detected but no sequence related to Azospirillum was found showing biasness of the growth medium rather than relative abundance of diazotrophs in the rhizosphere. CONCLUSION: The study provides a foundation for future research on focussed isolation of the Azoarcus and other diazotrophs found in higher abundance in the rhizosphere.

Single Intramuscular Methylprednisolone dose in Asthma and Chronic Obstructive Pulmonary Disease Patients on Discharge.

OBJECTIVE: In this pilot study we aimed to evaluate the safety of a single intramuscular methylprednisolone (IM) injection at the time of discharge as a replacement for oral steroid therapy for patients in our population with asthma or chronic obstructive pulmonary disease (COPD). METHODS: This proof-of-concept, open label clinical trial without randomisation was conducted at the Pulmonary Department of Ziauddin Hospital and University, Karachi from January 2018 to March 2018. Patients discharged after in-hospital treatment for exacerbations of either asthma or COPD were recruited for this study. Intramuscular injection of methylprednisolone was administered to these patients who were then followed-up after one week and one month. During that period, information was collected of the patients' self-report of any unscheduled emergency room visit, blood sugar and blood pressure control, symptoms suggestive of thrush, increase gastric acidity and weight gain. For the data analysis, frequency and percentages were calculated with SPSS version 21. RESULTS: A total of 30 patients aged 52.83 ± 14.27 years were recruited for this pilot study. At one month follow-up, no unscheduled emergency room visits were observed in all of the study patients. Symptoms suggestive of oral thrush were recorded in only 2 (6.7%) patients and weight gain was reported by only 5 (16.7%). Controlled blood sugar and blood pressure was reported by all the patients. No incidence of nocturnal symptoms, awakening and dyspepsia were reported. CONCLUSIONS: A single dose of methylprednisolone injection without any obvious side effects over one month among patients with asthma and COPD demonstrated a safe strategy for them.

Single-Equipment with Multiple-Application for an Automated Robot-Car Control System.

The integration of greater functionalities into vehicles increases the complexity of car-controlling. Many research efforts are dedicated to designing car-controlling systems that allow users to instruct the car just to show it what it should do; however, for non-expert users, controlling the car with a remote or a switch is complicated. So, keeping this in mind, this paper presents an Arduino based car-controlling system that no longer requires manual control of the cars. Two main contributions are presented in this work. Firstly, we show that the car can be controlled with hand-gestures, according to the movement and position of the hand. The hand-gesture system works with an Arduino Nano, accelerometer, and radio-frequency (RF) transmitter. The accelerometer (attached with the hand-glove) senses the acceleration forces that are produced by the hand movement, and it will transfer the data to the Arduino Nano that is placed on hand glove. After receiving the data, Arduino Nano will convert it into different angle values in ranges of 0°â»450° and send the data to the RF receiver of the Arduino Uno, which is placed on the car through the RF transmitter. Secondly, the proposed car system is to be controlled by an android based mobile-application with different modes (e.g., touch buttons mode, voice recognition mode). The mobile-application system is the extension of the hand-gesture system with the addition of Bluetooth module. In this case, whenever the user presses any of the touch buttons in the application, and/or gives voice commands, the corresponding signal is sent to the Arduino Uno. After receiving the signal, Arduino will check this against its predefined instructions for moving forward, backward, left, right, and brake; then it will send the command to the motor module to move the car in the corresponding direction. In addition, an automatic obstacle detection system is introduced to improve the safety measurements to avoid any hazards with the help of sensors placed at the front of the car. The proposed systems are designed as a lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. The experimental results prove that the proposed work has all in one capability (hand-gesture, touch buttons and voice-recognition with mobile-application, obstacle detection), is very easy to use, and can be easily assembled in a simple hardware circuit. We remark that the proposed systems can be implemented under real conditions at large-scale in the future, which will be useful in automobiles and robotics applications.

An Automation System for Controlling Streetlights and Monitoring Objects Using Arduino.

We present an Arduino-based automation system to control the streetlights based on solar rays and object's detection. We aim to design various systems to achieve the desired operations, which no longer require time-consuming manual switching of the streetlights. The proposed work is accomplished by using an Arduino microcontroller, a light dependent resistor (LDR) and infrared-sensors while, two main contributions are presented in this work. Firstly, we show that the streetlights can be controlled based on the night and object's detection. In which the streetlights automatically turn to DIM state at night-time and turn to HIGH state on object's detection, while during day-time the streetlights will remain OFF. Secondly, the proposed automated system is further extended to skip the DIM condition at night time, and streetlights turn ON based on the objects' detection only. In addition, an automatic door system is introduced to improve the safety measurements, and most importantly, a counter is set that will count the number of objects passed through the road. The proposed systems are designed at lab-scale prototype to experimentally validate the efficiency, reliability, and low-cost of the systems. We remark that the proposed systems can be easily tested and implemented under real conditions at large-scale in the near future, that will be useful in the future applications for automation systems and smart homes.