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Simple Summary: Hemorrhagic septicemia (HS) and mastitis are important diseases of South Asian dairy animals. Seventy S. aureus/Str. agalactiae-free lactating buffaloes (n = 45) and cows (n = 25) and fifty S. aureus/Str. agalactiae-positive lactating (early stage of lactation) buffaloes (n = 25) and cows (n = 25) were subjected to two doses of Montanide® adjuvant combined HS–mastitis vaccine with 21 days of interval. Vaccinated groups showed mean somatic cell counts and mastitis severity scores that were significantly lower (p < 0.05), whereas the milk yield was significantly higher (p < 0.05). In conclusion, this vaccine can be used as a potential preventive measure against HS and mastitis in dairy animals. Hemorrhagic septicemia (HS) and mastitis caused by Pasteurella (P.) multocida, Staphylococcus (S.) aureus and Streptococcus (Str.) agalactiae are important ailments of the dairy industry especially in South Asia. The present study evaluates the efficacy of a locally prepared hemorrhagic septicemia (HS) and mastitis combined vaccine. To this end, a total of 70 HS, S. aureus and Str. agalactiae-free lactating (early stage of lactation) buffaloes (n = 45) and cows (n = 25), and 50 lactating (early stage of lactation) cows (n = 25) and buffaloes (n = 25) positive for S. aureus/Str. agalactiae were subjected to two doses of HS–mastitis combined vaccine with an interval of 21 days. Antibody response was monitored by ELISA up to six months (180 days). Antibody titers against HS and mastitis were significantly (p ˂ 0.05) higher in vaccinated groups as compared to the non-vaccinated groups. Cumulative mean somatic cell counts and mastitis severity scores in vaccinated groups were significantly lower (p < 0.05), and milk yield was significantly higher (p < 0.05) than those in the respective non-vaccinated controls. In conclusion, Montanide®-adjuvanted HS–mastitis combined vaccine showed significant immunogenic effects in dairy cows and buffaloes. However, challenge studies remain overdue. [ABSTRACT FROM AUTHOR]

The fast spread of the COVID-19 over the world pressured scientists to find its cures. Especially, with the disastrous results, it engendered from human life losses to long-term impacts on infected people's health and the huge financial losses. In addition to the massive efforts made by researchers and medicals on finding safe, smart, fast, and efficient methods to accurately make an early diagnosis of the COVID-19. Some researchers focused on finding drugs to treat the disease and its symptoms, others worked on creating effective vaccines, while several concentrated on finding inhibitors for the key enzymes of the virus, to reduce its spreading and reproduction inside the human body. These enzymes' inhibitors are usually found in aliments, plants, fungi, or even in some drugs. Since these inhibitors slow and halt the replication of the virus in the human body, they can help fight it at an early stage saving the patient from death risk. Moreover, if the human body's immune system gets rid of the virus at the early stage it can be spared from the disastrous sequels it may leave inside the patient's body. Our research aims to find aliments and plants that are rich in these inhibitors. In this paper, we developed a deep learning application that is trained with various aliments, plants, and drugs to detect if a component contains SARS-CoV-2 key inhibitor(s) intending to help them find more sources containing these inhibitors. The application is trained to identify various sources rich in thirteen coronavirus-2 key inhibitors. The sources are currently just aliments, plants, and seeds and the identification is done by their names. [ABSTRACT FROM AUTHOR]

In this paper, a novel image forgery detection method is proposed based on the steerable pyramid transform (SPT) and local binary pattern (LBP). First, given a color image, we transform it in the YCbCr color space and apply the SPT transform on chrominance channels Cb and Cr, yielding a number of multi-scale and multi-oriented subbands. Then, we describe the texture in each SPT subband using LBP histograms. The histograms from each subband are concatenated to produce a feature vector. Finally, a support vector machine uses the feature vector to classify images into forged or authentic. The proposed method has been evaluated on three publicly available image databases. Our experimental results demonstrate the effectiveness of the proposed method and its superiority over some recent other methods. [ABSTRACT FROM AUTHOR]

With the increasing growth of media cloud technologies, web service technologies, and smartphones equipped sensors, a number of collaborative media services are being built for ubiquitous user access. Currently, collaborative services are being used in several areas like healthcare, defense, education, and so forth. However, due to the challenge of providing such service to users in terms of computations, communications, processing, and storage, there is a growing need for an infrastructure to have on-demand access to a shared pool of configurable computing resources (e.g., networks, storages, servers, applications, and services). Cloud computing is such a paradigm or infrastructure to provide configurable platform to support the collaborative service. In this paper, we present the corresponding framework of collaborative media service for efficient collaboration between caregivers and healthcare professionals. The experimental results not only showed our solution is more efficient than the similar system but also proved that our solution can work well for web service-based collaborative environment. [ABSTRACT FROM AUTHOR]

Highlights: [•] Multiple surveys on m-learning are conducted among the students and the academics. [•] Great enthusiasm in augmented reality technology via m-learning is found. [•] Students are highly in favor of regular update of course materials via m-devices. [•] The learners and educators have expressed some security and coverage concerns. [Copyright &y& Elsevier]

In this paper, we propose a multimodal biometric system with high security level based on finger and palm print. Verification/identification is performed by fusing features form different finger and palm prints. The features are extracted using Zernike moments (ZM) invariant algorithm. Fisher's ratio ( F-ratio) is used to select the optimum moments. The experimental results show that ring fingerprint, palm print, middle fingerprint, and thumb fingerprint give better performance than other fingerprints. Applying F-ratio on these four prints can recognize all the 50 individuals participated in the experiments. [ABSTRACT FROM AUTHOR]

This paper investigates and compares the performance of local descriptors for race classification from face images. Two powerful types of local descriptors have been considered in this study: Local Binary Patterns (LBP) and Weber Local Descriptors (WLD). First, we investigate the performance of LBP and WLD separately and experiment with different parameter values to optimize race classification. Second, we apply the Kruskal-Wallis feature selection algorithm to select a subset of more "discriminative" bins from the LBP and WLD histograms. Finally, we fuse LBP and WLD, both at the feature and score levels, to further improve race classification accuracy. For classification, we have considered the minimum distance classifier and experimented with three distance measures: City-block, Euclidean, and Chi-square. We have performed extensive experiments and comparisons using five race groups from the FERET database. Our experimental results indicate that (i) using the Kruskal-Wallis feature selection, (ii) fusing LBP with WLD at the feature level, and (iii) using the City-block distance for classification, outperforms LBP and WLD alone as well as methods based on holistic features such as Principal Component Analysis (PCA) and LBP or WLD (i.e., applied globally). [ABSTRACT FROM AUTHOR]

The objective of the present work is for assessing ergonomics-based IoT (Internet of Things) related healthcare issues with the use of a popular multi-criteria decision-making technique named the analytic hierarchy process (AHP). Multiple criteria decision making (MCDM) is a technique that combines alternative performance across numerous contradicting, qualitative, and/or quantitative criteria, resulting in a solution requiring a consensus. The AHP is a flexible strategy for organizing and simplifying complex MCDM concerns by disassembling a compound decision problem into an ordered array of relational decision components (evaluation criteria, sub-criteria, and substitutions). A total of twelve IoT-related ergonomics-based healthcare issues have been recognized as Lumbago (lower backache), Cervicalgia (neck ache), shoulder pain; digital eye strain, hearing impairment, carpal tunnel syndrome; distress, exhaustion, depression; obesity, high blood pressure, hyperglycemia. "Distress" has proven itself the most critical IoT-related ergonomics-based healthcare issue, followed by obesity, depression, and exhaustion. These IoT-related ergonomics-based healthcare issues in four categories (excruciating issues, eye-ear-nerve issues, psychosocial issues, and persistent issues) have been compared and ranked. Based on calculated mathematical values, "psychosocial issues" have been ranked in the first position followed by "persistent issues" and "eye-ear-nerve issues". In several industrial systems, the results may be of vital importance for increasing the efficiency of human force, particularly a human–computer interface for prolonged hours. [ABSTRACT FROM AUTHOR]

Equine infectious anemia (EIA) is a cosmopolitan fatal recurring retroviral disease of equine family. Keeping in view the importance of the disease, this study was planned and executed to investigate the presence of EIA in 5 draught equine populated metropolises (Lahore, Gujranwala, Faisalabad, Multan and Bahawalpur) of Punjab, Pakistan. A cross-sectional epidemiological survey was conducted and 430 blood and serum samples were randomly collected from 332 donkeys, 65 horses, and 33 mules along with epidemiological information. Samples were then transported to the laboratory for analysis. Blood samples were tested to determine erythrocyte indices for establishing the type of anemia. Serum samples were analyzed for the presence of antibodies against EIA virus through a commercial ELISA. Although, erythrocyte indices indicated towards presence of anemia in equines, no serum sample was found positive on ELISA. This is a first ever study in Pakistan where presence of EIA was investigated over a wide geographic region and indicated towards the possible disease free status of the selected equine population. [ABSTRACT FROM AUTHOR]

Background: Glanders is a contagious and fatal zoonotic disease of solipeds caused by the Gram-negative bacterium Burkholderia (B.) mallei. Although regulations call for culling of diseased animals, certain situations e.g. wild life conservation, highly valuable breeding stock, could benefit from effective treatment schemes and post-exposure prophylaxis. Results: Twenty three culture positive glanderous horses were successfully treated during a confined outbreak by applying a treatment protocol of 12 weeks duration based on the parenteral administration of enrofloxacin and trimethoprim plus sulfadiazine, followed by the oral administration of doxycycline. Induction of immunosupression in six randomly chosen horses after completion of treatment did not lead to recrudescence of disease. Conclusion: This study demonstrates that long term treatment of glanderous horses with a combination of various antibiotics seems to eliminate the agent from the organism. However, more studies are needed to test the effectiveness of this treatment regime on B. mallei strains from different endemic regions. Due to its cost and duration, this treatment can only be an option in certain situations and should not replace the current "testing and culling" policy, in conjunction with adequate compensation to prevent spreading of disease. [ABSTRACT FROM AUTHOR]

Background: Laparoscopic surgery has revolutionized the way it is performed for an increasing number of patients. In this study, we determined the various reasons for conversion of laparoscopic cholecystectomy in our setup. Material & Methods: This was a descriptive study, conducted at Ghulam Muhammad Mahar Medical College Hospital and Hira Medical Centre, Sukkur, from January 2006 to June 2010. The study included 1224 patients with symptomatic cholelithiasis, who underwent laparoscopic cholecystectomy. All patients were operated by experienced laparoscopic surgeons with experience of more than 300 Laparoscopic cholecystectomies. Cases that required conversion from laparoscopic to open surgery were analyzed and the factors responsible for such conversion were studied. Results: The mean age of patients was 45 years and male to female ratio 1:4. The mean operating time was 45 minutes and average hospital stay was 2 days. Out of 1224 patients, 8(0.65%) required open conversion. Factors responsible for open conversion were dense adhesions in 3(0.25%), fibrosed gall-bladder with cholecystoduodenal fistula in 1(0.081%), CBD injury 1(0.081%), Mirizzi's syndrome 1(0.081%), and instrument failure as well as power breakdown with backup failure in 2(0.16%) conversions. Conclusion: Open conversion rate of laparoscopic cholecystectomy in this study was 0.65%. The commonest cause of conversion was dense adhesions around the gall-bladder. Preventable factors like instrument failure or power breakdown can be addressed by a reliable back up. [ABSTRACT FROM AUTHOR]

Background and objective: There has been a growing interest in objective assessment of speech in dysphonic patients for the classification of the type and severity of voice pathologies using automatic speech recognition (ASR). The aim of this work was to study the accuracy of the conventional ASR system (with Mel frequency cepstral coefficients (MFCCs) based front end and hidden Markov model (HMM) based back end) in recognizing the speech characteristics of people with pathological voice. Materials and methods: The speech samples of 62 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits were taken as an input. The distribution of the first four formants of the vowel /a/ was extracted to examine deviation of the formants from normal. Results: There was 100% recognition accuracy obtained for Arabic digits spoken by normal speakers. However, there was a significant loss of accuracy in the classifications while spoken by voice disordered subjects. Moreover, no significant improvement in ASR performance was achieved after assessing a subset of the individuals with disordered voices who underwent treatment. Conclusion: The results of this study revealed that the current ASR technique is not a reliable tool in recognizing the speech of dysphonic patients. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the unique pharyngeal and uvular consonants of Arabic from the point of view of automatic speech recognition (ASR). Comparisons of the recognition error rates for these phonemes are analyzed in five experiments that involve different combinations of native and non-native Arabic speakers. The most three confusing consonants for every investigated consonant are discussed. All experiments use the Hidden Markov Model Toolkit (HTK) and the Language Data Consortium (LDC) WestPoint Modern Standard Arabic (MSA) database. Results confirm that these Arabic distinct consonants are a major source of difficulty for Arabic ASR. While the recognition rate for certain of these unique consonants such as // can drop below 35% when uttered by non-native speakers, there is advantage to include non-native speakers in ASR. Besides, regional differences in pronunciation of MSA by native Arabic speakers require the attention of Arabic ASR research. [Copyright &y& Elsevier]

Abstract: The present study describes epidemic resurgence of a goat-specific syndrome simulating goat dermatitis at six different places in Pakistani Punjab province and at two places in Sindh province after a lapse of 56 years. The occurrence of the syndrome was limited to February thru July 2004; being absent thereafter till May 2007. Cumulative attack rate of the syndrome was 0.2%; 37 of 181 goats in 11 flocks contracting the syndrome over a 6-month outbreak period. Age-specific attack rates were higher (54.97) in goats younger than 1 year than in goats older than this. Similarly, the overall case fatality rate was higher (88.46%) in goats younger than 1 year than in those older than 1 year (81.81%). None of the cohort sheep in flocks (n =11) comprising both goat and sheep developed the syndrome. Significant clinical signs included fever (41.1–42.2°C), generalized appearance of prickly heat-like tiny reddish dermal papules which successively transformed into thick rubbery intradermal nodules, necrotic blackish striated ulcers, and punched dermal ulcers covered with saucer-shaped black scabs eventually shedding after 38–60 days. Smaller grayish-white nodules were also seen on tongue, gums and nares. Vesicles and pustules were not seen at any stage of the disease. Chicken embryos inoculated with the filtrate via chorio-allantoic membrane route did not support the growth of any microorganism. When the filtrate prepared from the nodules of naturally affected goats was smeared onto the scarified inner skin of the ear of Beetal goats previously, vaccinated against goat-pox, the inoculated goats developed hyperemia and pyrexia (40.5–42.2°C) after 7–8 days followed by the appearance of dermal lesions compatible with those of spontaneously occurring syndrome. Intradermal inoculation of filtrate on the costal area of goats resulted in appearance of skin swellings and pyrexia 3 days postinoculation. This was followed by successive appearance of typical skin lesions of the syndrome. Goats inoculated with filtrate prepared from dermal lesions and papules in the wake of recovery from the syndrome, inoculated sheep, rabbits and guinea pigs did not develop skin lesions or fever. A 6-day treatment protocol based on the use of antibiotic, antipyretic, VitAD3E and topical antiseptic with and without antiviral (ribavirin) had no effect on the severity as well as final outcome of the syndrome. Vaccination against goat-pox did not stop occurrence of new cases. Contrarily, autogenous caprination with supernatant prepared from rubbery nodules of affected goats stopped the further progression of the disease to yet unaffected goats. The recrudescence of a contagious fatal goat-specific syndrome over a wide geographical region after an apparent lapse of 56 years signifies the need to include this goat disease in active surveillance program of caprine diseases. [Copyright &y& Elsevier]

Abstract: We identified clinical disorders of all 200 city-dwelling cart pulling male camels attending the Veterinary Teaching Hospital, University of Agriculture, Faisalabad, Pakistan during a 7-year period (1993–1999). Data were collected prospectively on a predesigned form and collated. Diagnoses of different diseases/disorders were based on clinical examination supplemented with relevant laboratory tests. A total of 463 entries of 34 different clinical diseases/disorders were recorded. Sarcoptic mange (35% of 200 camels) followed by anhidrosis (23%) and trypanosomosis (19%) were the three most frequently encountered disorders. The body system most often involved was the integument (31%) followed by gastrointestinal (21%), locomotory (12%), thermoregulatory (6%), blood (6%), urogenital (6%), lymphatic (3%), nervous (3%), respiratory (3%) and ocular (3%). [Copyright &y& Elsevier]

Abstract: The present study was planned to investigate the ethnoveterinary methods practiced by the owners of pneumatic-cart pulling camels in Faisalabad Metropolis (Pakistan). During a 7-year-period (November 1992–November 1999), 200 owners of draught camels working in the city were interviewed. Information concerning the ethnoveterinary practices for the treatment of common disorders of digestive tract (indigestion, colic and diarrhea), respiratory tract (cold/rhinitis, pneumonia), skin problems (mange, ulceration of nostrils with or without nasal myiasis, ticks and lice, harness sores), systemic states (fever, ze/rba/d, anhidrosis) and preventive therapy of indigestion and halitosis was collected through interviews and collated with those documented for the treatment of desert-dwelling camels. Familiarity of owners with two traditional methods of surra (trypanosomiasis) diagnosis (‘Sand-ball test’ and ‘Hair-stick test’) known to pastorilists was also probed. In addition, the dose and frequency of use of common salt was investigated. Traditional inputs utilized by the camel owners included various plant products, insecticides, sulphur, sump oil, common salt, aspirin, naphthalene balls and milk fat. Different owners used different combinations of traditional drugs for the treatment of disorders/conditions investigated. None of the camel owners was found familiar with the ‘Sand-ball test’ or ‘Hair-stick test’ of trypanosomiasis diagnosis. For the prevention of indigestion and halitosis all camel owners had practiced administration of ‘massaulas’ (physic drench/balls) along with common salt (average 250g) on weekly basis. Firing had not been used by any owner. In general, the ethnoveterinary treatment practices used by the owners of city-dwelling camels appear to be different from those documented for the treatment of diseases of desert-dwelling camels. [Copyright &y& Elsevier]

The increase in chronic diseases has affected the countries' health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens' needs. Deep learning techniques is a technological solution that has been used in healthcare lately. Nowadays, with the increase in chips processing capabilities, increase size of data, and the progress in deep learning research, healthcare applications have been proposed to provide citizens' health needs. In addition, a big amount of data is generated every day. Development in Internet of Things, gadgets, and phones has allowed the access to multimedia data. Data such as images, video, audio and text are used as input of applications based on deep learning methods to support healthcare system to diagnose, predict, or treat patients. This review pretends to give an overview of proposed healthcare solutions based on deep learning techniques using multimedia data. We show the use of deep learning in healthcare, explain the different types of multimedia data, show some relevant deep learning multimedia applications in healthcare, and highlight some challenges in this research area. [ABSTRACT FROM AUTHOR]

Infrared and visible image fusion aims to generate informative images by leveraging the distinctive strengths of infrared and visible modalities. These fused images play a crucial role in subsequent downstream tasks, including object detection, recognition, and segmentation. However, complementary information is often difficult to extract. Existing generative adversarial network-based methods generate fused images by modifying the distribution of source images' features to preserve instances and texture details in both infrared and visible images. Nevertheless, these approaches may result in a degradation of the fused image quality when the original image quality is low. Considering the balance of information from different modalities can improve the quality of the fused image. Hence, we introduce CABnet, a Channel Attention dual adversarial Balancing network. CABnet incorporates a channel attention mechanism to capture crucial channel features, thereby, enhancing complementary information. It also employs an adaptive factor to control the mixing distribution of infrared and visible images, which ensures the preservation of instances and texture details during the adversarial process. To enhance efficiency and reduce reliance on manual labeling, our training process adopts a semi-supervised learning strategy. Through qualitative and quantitative experiments across multiple datasets, CABnet surpasses existing state-of-the-art methods in fusion performance, notably achieving a 51.3% enhancement in signal to noise ratio and a 13.4% improvement in standard deviation. • Enhancing feature extraction capability and eliminating redundant features by channel attention module. • Controlling the mixed distribution by adaptive balance module. • Converging faster by an enhanced loss function. [ABSTRACT FROM AUTHOR]

With the advancement in digital communication and artificial intelligence-based applications, the emphasis on information security has intensified. Traditional authentication techniques like passwords, fingerprints, and facial recognition present risks of unauthorized access and duplication. The distinctive nature of electrocardiogram (ECG) signals is drawing considerable attention as an identity verification approach. However, current ECG-based identification methods tend to possess intricate preprocessing phases and excessively rely on the accuracy of fiducial point detection. This prevents a comprehensive capture of the information within the ECG signals, thereby affecting the accuracy of identification. To address these issues, we introduce RandSaD (Random-window Split attention Dual-path), an innovative ECG identity recognition model. RandSaD combines rand om window extraction with a d ual-path residual neural network and integrates the s plit a ttention (SpAt) mechanism. During the preprocessing stage, RandSaD operates directly on wavelet-denoised ECG signals. It extracts random windows with the objective of preserving the intrinsic characteristics of the ECG signal while minimizing information loss. Subsequently, we integrate a dual-path residual network integrated with SpAt. Each path can independently extract features of a specific scale or type, enhancing the capability of the model to extract features. The SpAt amplifies the capacity of the model to distinguish and merge information from different channels. Experimental results indicate that the RandSaD model achieves a 99.6% accuracy rate in ECG identity recognition tasks. [ABSTRACT FROM AUTHOR]

With the development of neural networks, identity recognition techniques utilizing human physiological signals have found applications in healthcare systems. However, many current physiological signal datasets have to spend a lot of resources in the pre-labeling phase, which leads to longer training times. To reduce the cost of labeling, we propose a self-supervised time-series representation learning framework based on contrastive positive-unlabeled learning (TS − CPUL), to learn the representations of temporal neighborhood sequences. To obtain the representations, the unit root test divides the unlabeled time series into neighborhood samples and unlabeled samples. Secondly, we use neighborhood sample representations and unlabeled sample representations to do self-supervised contrastive learning and positive-unlabeled learning with a negative sample selector. Both approaches can deepen the model's understanding of the sample representation. Lastly, to enhance the model's capability in discriminating between ambiguous sample pairs, we design the distance-polarized matrix. The proposed TS-CPUL is evaluated through experiments on three physiological signal datasets. The results demonstrate its effectiveness in understanding time-series representations and performing well in classification. For the identity recognition task, we achieve accuracy comparable to the supervised model by fine-tuning the model with only 10% of the labeled data. [ABSTRACT FROM AUTHOR]

Traffic forecasting plays a vital role in the management of urban road networks and the development of intelligent transportation systems. To effectively capture spatial and temporal dependencies within traffic data, Graph Neural Networks (GNNs) emerge by combining recurrent neural networks and dynamic graph learning. However, traditional GNNs face challenges in efficiently memorizing patterns at different levels of these dependencies, including long-term shared patterns and short-term specific patterns. We propose a traffic forecasting model, the Multi-level Graph Memory Network Cluster Convolutional Recurrent Network (MMNCCRN). The MMNCCRN consists of four modules: Encoder Module (EM), Attention Module (AM), Memory Network Cluster Module (MNCM), and Decoder Module (DM). To enhance the model's performance, AM incorporates a concise yet efficient attention mechanism that augments important information in the output of EM. This alleviates the pressure on MNCM to identify patterns at different levels. Meanwhile, in MNCM, we extensively leverage memory networks and introduce the concept of clustering. By storing and memorizing patterns implicit in spatial and temporal dependencies, MNCM assists the model in learning underlying graph structures and discovering new hidden patterns. Experimental results show that our model outperforms others in most metrics on four datasets. • Specific patterns and shared patterns are implicit in traffic data. • Memory network cluster can memorize, and utilize patterns at multiple levels. • An effective attention machanism helps alleviate the memory burden of the cluster. • Memory items are collectively used to represent the graph structure. • Memory network cluster is more effective than simply expanding one single network. [ABSTRACT FROM AUTHOR]

In this paper, we report first-principles spin-polarized density functional theory calculations for exploring the effect of aliovalent Mo and C dopants on the electronic properties and photocatalysis potential of doped modifications of wide-bandgap cubic perovskite oxide BaHfO3 for water splitting. The structural and thermodynamic properties are computed by using the generalized gradient approximation, whereas the modified Becke-Johnson local density approximation is used to calculate the electronic structures of pristine, cation (Mo), and anion (C) monodoped and cation-anion (Mo, C) codoped BaHfO3. The spin-polarized calculations reveal that substitutional dopants CO and MoHf in the BaHfO3 lattice are thermodynamically stable. The incorporation of C in the O site reduces the bandgap of BaHfO3 and acts as a double-acceptor system, whereas a metallic character is obtained when Mo is doped into the Hf site giving rise to a double-donor system. We show that the acceptor and donor states of the C- and Mo-monodoped BaHfO3 can be passivated by (Mo, C) codoping at nearest-neighbor Hf and O sites of the BaHfO3 lattice, respectively. Analysis of the calculated electronic structure and optical absorption of (Mo, C) codoped BaHfO3 with reference to the H2O oxidation and reduction potentials reveals that this system is a suitable candidate for efficient splitting of water through photocatalysis in the visible region of the electromagnetic spectrum. [ABSTRACT FROM AUTHOR]

Abstract: The present study was planned to evaluate the possible transmission of ochratoxin A (OTA) in serum and targeted organs of broilers fed on two levels (500 and 1000ppb) this toxin in the presence or absence of a toxin deactivator (containing a mycotoxin deactivating yeast Trichosporon mycotoxinivorans) at two inclusion levels (1 and 2kg/ton of feed) to 270day-old broiler chicks divided into nine groups (A–I) over a 42days period. Serum samples were collected at 14, 28 and 42nd day of experiment, whereas, liver and kidney tissues were obtained from broilers slaughtered at 42nd day of experiment. The highest OTA levels were detected in serum, livers and kidneys of OTA treated groups without supplementation of toxin deactivator (groups D and G) at day 42 of experiment, while the residues were significantly (P <0.01) lower in treatment groups (F and I) supplemented with toxin deactivator at 2kg/ton of feed. The order of OTA level was serum>kidneys>liver. [Copyright &y& Elsevier]

This preliminary report describes the occurrence of acute pulmonary emphysema cum pulmonary edema ensuing in extensive subcutaneous emphysematous swellings in a dairy buffalo (Bubalus bubalis) apparently associated with a sudden shift from berseem (Trifolium alexendrinum) to Brassica juncea fodder. Tachypnea, expiratory dyspnea, open-mouth breathing, loud expiratory grunt with abdominal lift, and crackles in ventral aspects of the lungs with normal rectal temperature characterized the condition clinically. A substantial improvement was noted 3 h after treatment with parenteral administration of prednisolone plus dexamethasone, diclofenac sodium, and furosemide. Per os administration of chlortetracycline was undertaken after dyspnea had abated. Extensive subcutaneous swellings developed as sequel on withers, back, axillae, and thorax, was deflated using a hypodermic needle. The buffalo completely recovered in 10 days. As far as could be ascertained, this is the first report dwelling on acute pulmonary emphysema cum pulmonary edema. [ABSTRACT FROM AUTHOR]

A cross-sectional study has been carried out in order to determine the prevalence of Trypanosoma evansi infection in susceptible hosts in the Punjab region (Pakistan). A total of 170 equines and 150 dromedary camels were examined. Five (3.3%) and 6 (4%) camels were positive at parasitological and serological examination, respectively. None of the equines tested positive at any method. These results seem to indicate that T. evansi infection has a relatively low prevalence in the Punjab region. However, efforts must be done in order to establish control measures in affected herds and avoid dissemination of the disease. [ABSTRACT FROM AUTHOR]

We develop the solution procedures to solve the bipolar fuzzy linear system of equations (BFLSEs) with some iterative methods namely Richardson method, extrapolated Richardson (ER) method, Jacobi method, Jacobi over-relaxation (JOR) method, Gauss–Seidel (GS) method, extrapolated Gauss-Seidel (EGS) method and successive over-relaxation (SOR) method. Moreover, we discuss the properties of convergence of these iterative methods. By showing the validity of these methods, an example having exact solution is described. The numerical computation shows that the SOR method with ω = 1.25 is more accurate as compared to the other iterative methods. [ABSTRACT FROM AUTHOR]

Fake news is frequently disseminated through social media, which significantly impacts public perception and individual decision-making. Accurate identification of fake news on social media is usually time-consuming, laborious, and difficult. Although the leveraging of machine learning technologies can facilitate automated authenticity checks, the time-sensitive and voluminous nature of the data brings considerable challenge for fake news detection. To address this issue, this paper proposes a quantum multimodal fusion-based model for fake news detection (QMFND). QMFND integrates the extracted images and textual features, and passes them through a proposed quantum convolutional neural network (QCNN) to obtain discriminative results. By testing QMFND on two social media datasets, Gossip and Politifact, it is proved that its detection performance is equal to or even surpasses that of classical models. The effects of various parameters are further investigated. The QCNN not only has good expressibility and entangling capability but also has good robustness against quantum noise. The code is available at • Quantum multimodal fusion-based fake news detection model is proposed for social media. • Amplitude encoding is adopted to encode multimodal features into a VQC. • QCNN is proposed for efficient training of encoded multimodal data. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate. [ABSTRACT FROM AUTHOR]

Glanders is highly contagious and zoonotic bacterial disease of equines caused by Burkholderia mallei. Since many decades, glanders is endemic in eastern parts (Punjab) of Pakistan and has no information of disease in the north-western region (Khyber Pakhtunkhwa – KPK). Therefore, present study was aimed to determine the seroprevalence and associated risk factors in two draft equine populated districts (Charsada and Peshawar) of KPK. A total of 393 equine samples originating from different localities of study area were investigated for anti-B. mallei antibodies (glanders) using indirect enzyme linked immunosorbent assay (ID Vet, Grabels France). The overall seroprevalence was found to be 3.1% (95% CI: 1.6-5.3) which remained insignificantly (χ2 0.421, P=0.516) between the two districts. In Charsada, prevalence estimates varied significantly (χ2 11.986, P=0.017) among 5 localities and found insignificant (χ2 1.287, P=0.732) in different sampling areas of Peshawar district. Donkeys were more seropositive (4.1%; 4/195) than horses (2.1%; 8/194) which did not differ statistically (χ2 1.397, P=0.237) and all mules were tested negative. Prevalence slight increased with age of animals (>10 years) and with poor body conditions. Donkeys were more test positive (OR: 2.05, 95% CI: 0.61-6.94) than horses and similarly animals managed on communal feeding (OR: 1.84, 95% CI: 0.23-14.5) and water troughs (OR: 1.65, 95% CI: 0.21-13.04) had more chances of contracting glanders. Multivariable model indicated donkeys (OR: 4.58, 95% CI: 1.0, 20.9) and equines above 10 years of age (OR: 3.10, 95% CI: 0.7-13.3) were more likely to test positive for glanders. In conclusion, present study provides prevalence estimates of equine glanders in KPK regions, which appears to be an area of endemicity. Future large-scale investigations are seriously warranted in other equine populated districts before implementing an effective control plan for glanders. [ABSTRACT FROM AUTHOR]

This paper presents a new scheme for solving m -polar fuzzy system of linear equations (m -PFSLEs) by using LU decomposition method. We assume the coefficient matrix of the system is symmetric positive definite, and we discuss this point in detail with some numerical examples. Furthermore, we investigate the inconsistent m × n m -polar fuzzy matrix equation (m -PFME) and find the least square solution (LSS) of this system by using generalized inverse matrix theory. Moreover, we discuss the strong solution of m -polar fuzzy LSS of the inconsistent m -PFME. In the end, we present a numerical example to illustrate our approach. [ABSTRACT FROM AUTHOR]

This paper proposes cloudlet-based intelligent agents for energy crowdsourcing from autonomous electric vehicles (AEVs). Existing energy crowdsourcing mechanisms focus on load shedding and cost savings, but lack incentive models for strategically behaving agents. In the proposed model, crowdsourcing agents residing on the edge network communicate with AEVs to stimulate them to participate in providing energy to the grid during peak time periods. The focus of this paper is on the development of incentive mechanisms that allow the agents to engage in an auctioning process according to their energy contribution. Through theoretical analysis and simulations, we evaluate the performance of the proposed model with respect to individual rationality, computational efficiency, and truthfulness. [ABSTRACT FROM AUTHOR]

Autonomous electric vehicles (AEVs) are gaining ground around the world. They are equipped with intelligent decision-making capabilities that allow them to optimize on their battery usage and engage in energy trading for profit maximization. This paper envisions a market model, where energy aggregators considers buying and selling electricity from AEVs. In this system, the aggregators reside on cloudlets at the edge of a cloud computing system for prompt communication and negotiation with AEVs. Whether they are parked or en-route, AEVs can be crowdsourced to provide energy to consumers at needed times. This study proposes double auction models to incentivize AEV agents to participate in the market based on their time preferences and energy pricing. The proposed system implements dynamic pricing structures, which are applicable to various scenarios of energy trading. To evaluate the proposed system, this paper provides extensive theoretical analysis and simulation experiments to demonstrate that the proposed auctioning models are computationally efficient, truthful, individually rational, and budget balanced. [ABSTRACT FROM AUTHOR]

Federated learning is an effective technique to solve the problem of information fusion and information sharing in intelligent vehicle networking. However, most of the existing federated learning algorithms generally have the risk of privacy leakage. To address this security risk, this paper proposes a novel personalized federated learning with privacy preservation (PDP-PFL) algorithm based on information fusion. In the first stage of its execution, the new algorithm achieves personalized privacy protection by grading users' privacy based on their privacy preferences and adding noise that satisfies their privacy preferences. In the second stage of its execution, PDP-PFL performs collaborative training of deep models among different in-vehicle terminals for personalized learning, using a lightweight dynamic convolutional network architecture without sharing the local data of each terminal. Instead of sharing all the parameters of the model as in standard federated learning, PDP-PFL keeps the last layer local, thus adding another layer of data confidentiality and making it difficult for the adversary to infer the image of the target vehicle terminal. It trains a personalized model for each vehicle terminal by "local fine-tuning". Based on experiments, it is shown that the accuracy of the proposed new algorithm for PDP-PFL calculation can be comparable to or better than that of the FedAvg algorithm and the FedBN algorithm, while further enhancing the protection of data privacy. • A new personalized differential privacy algorithm (PDP) is proposed. • A new Personalized Federated Learning (PFL) with PDP based on information fusion is proposed. • The PDP uses a lightweight dynamic convolutional network for greater practicability. • It provides balance and effective use of computing load among various in-vehicle terminals. [ABSTRACT FROM AUTHOR]

Automating polyps' detection and segmentation is very helpful in achieving early detection and potentially effective treatment of colon cancer. Because of the constant rise in processing power at a reasonable cost and the availability of training data, deep learning more than ever is becoming very powerful and increasingly widespread in many domains, including computer vision and medical image processing and segmentation. In this paper, we propose a novel method for polyp image segmentation using convolutional neural networks (CNNs) and utilizing multi-parallel U-Net encoder architecture. The proposed method uses multiple encoders that utilize pre-trained CNNs to enrich the extracted features. Skip connections from these parallel encoders are concatenated and propagated to the decoder in partially and fully meshed fashions allowing for data from shallow to deeper layers in the network to be exchanged and vice versa. We trained and tested the method on five publicly available datasets: Kvasir, CVC-Clinic DB, CVC-Colon DB, CVC-T, and ETIS-Larib, which are well-known benchmark datasets for polyp image segmentation. We tested the performance of the method on these five datasets using multiple testing scenarios including the effect of using attention, vision transformers, and multiple decoders. Experimental results showed that the proposed method outperforms other state-of-the-art methods on two of the used benchmark datasets and ranked second on a third. • A novel CNN-based method using parallel encoders for polyp segmentation is proposed. • Combinations of two and three parallel encoders, and decoders with different pre-trained networks are investigated. • The effect of attention and vision transformers is examined. • Five publicly available polyp image databases were used in the experiments. • Dual encoder with attention block and partial mesh skip connections achieved the best result. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) has become a crucial element for smart healthcare applications due to the fast adoption of wearable sensors and mobile technologies. Most of the existing human activity recognition frameworks deal with a single modality of data that degrades the reliability and recognition accuracy of the system for heterogeneous data sources. In this article, we propose a multi-level feature fusion technique for multimodal human activity recognition using multi-head Convolutional Neural Network (CNN) with Convolution Block Attention Module (CBAM) to process the visual data and Convolutional Long Short Term Memory (ConvLSTM) for dealing with the time-sensitive multi-source sensor information. The architecture is developed to be able to analyze and retrieve channel and spatial dimension features through the use of three branches of CNN along with CBAM for visual information. The ConvLSTM network is designed to capture temporal features from the multiple sensors' time-series data for efficient activity recognition. An open-access multimodal HAR dataset named UP-Fall detection dataset is utilized in experiments and evaluations to measure the performance of the developed fusion architecture. Finally, we deployed an Internet of Things (IoT) system to test the proposed fusion network in real-world smart healthcare application scenarios. The findings from the experimental results reveal that the developed multimodal HAR framework surpasses the existing state-of-the-art methods in terms of multiple performance metrics. • Multimodal data fusion for HAR through multi-head architecture, and ConvLSTM. • Retrieving the channel and spatial dimension features via CBAM. • Model deployment in IoT networks for gauging performance in IoHT. • Tests on the UP-Fall detection dataset demonstrate state-of-the-art results. [ABSTRACT FROM AUTHOR]

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. [ABSTRACT FROM AUTHOR]

COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient's life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model. [ABSTRACT FROM AUTHOR]

The emerging Cyber–Physical Systems (CPSs) provide a number of services ranging from smart homes to elderly care. With a CPS, a user’s (elderly person’s) interactions with smart home appliances are sensed, collected, and shared in physical spaces. This interaction is then delivered to cyberspaces for processing in order to monitor energy efficiency. In this paper, we propose an energy-efficient cyber–physical smart home system for monitoring the elderly that uses the potential of cloud computing and big data technologies. Given that an elderly person may need assistance with the complex activities of maintaining energy efficiency, a smart multimedia-enabled middleware assistant is proposed that enables him/her to observe different energy-efficient processes, control smart home appliances through gestures, receive notifications regarding appliance statuses, and share multimedia messages with a community of interest. For data processing, robust and low-dimensional discriminative features are extracted in a server from the data available via sensors and multimedia. Events are also automatically classified in the server to assist caregiver decision-making. To save energy, an automatic switch on/off control system using speech or gesture is developed. Keywords are detected from the speech and used as control commands. Experimental results show the encouraging potential of deploying this framework at a large scale. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effect of Arabic phonemes on the performance of speaker recognition systems. The investigation reveals that some Arabic phonemes have a strong effect on the recognition rate of such systems. The performance of speaker recognition systems can be improved and their execution time can be reduced by utilizing this finding. Additionally, this finding can be used by segmenting the most effective phonemes for speaker recognition from the utterance, using only the segmented part of the speech for speaker recognition. It can also be used in designing the text to be used in high-performance speaker recognition systems. From our investigation, we find that the recognition rates of Arabic vowels were all above 80%, whereas the recognition rates for the consonants varied from very low (14%) to very high (94%), with the latter achieved by a pharyngeal consonant followed by the two nasal phonemes, which achieved recognition rates above 80%. Four more consonants had recognition rates between 70% and 80%. We show that by utilizing these findings and by designing the text carefully, we can build a high-performance speaker recognition system. [ABSTRACT FROM AUTHOR]

Local features for any pattern recognition system are based on the information extracted locally. In this paper, a local feature extraction technique was developed. This feature was extracted in the time–frequency plain by taking the moving average on the diagonal directions of the time–frequency plane. This feature captured the time–frequency events producing a unique pattern for each speaker that can be viewed as a voice print of the speaker. Hence, we referred to this technique as voice print-based local feature. The proposed feature was compared to other features including mel-frequency cepstral coefficient (MFCC) for speaker recognition using two different databases. One of the databases used in the comparison is a subset of an LDC database that consisted of two short sentences uttered by 182 speakers. The proposed feature attained 98.35% recognition rate compared to 96.7% for MFCC using the LDC subset. [ABSTRACT FROM PUBLISHER]

Due to the increasing prices of cement and its harmful effect on the environment, the use of cement has become highly unsustainable in concrete. There is a considerable need for promoting the use of cement replacement materials. This study investigates the effect of variable percentages of metakaolin (MK) on the mechanical and durability performance of concrete. Kaolin clay (KC) was used in the current research to prepare the MK by the calcination process; it was ground in a ball mill to its maximum achievable fineness value of 2550 m2/Kg. Four replacement levels of MK, i.e., 5%, 10%, 15%, and 20% by weight of cement, in addition to control samples, at a constant water-to-cement (w/c) ratio of 0.55 were used. For evaluating the mechanical and durability performance, 27 cubes (6 in. × 6 in. × 6 in.) and 6 cylinders (3.875 in. diameter, 2 in. height) were cast for each mix. These samples were tested for compressive strength under standard conditions and in an acidic environment, in addition to being subjected to water permeability, sorptivity, and water absorption tests. Chemical analysis revealed that MK could be used as pozzolana as per the American Society for Testing and Materials (ASTM C 618:2003). The results demonstrated an increased compressive strength of concrete owing to an increased percentage of MK in the mix with aging. In particular, the concrete having 20% MK after curing under standard conditions exhibited 33.43% higher compressive strength at 90 days as compared to similarly aged control concrete. However, with increasing MK, the workability of concrete decreased drastically. After being subjected to an acid attack (immersing concrete cubes in 2% sulfuric acid solution), the samples exhibited a significant decrease in compressive strength at 90 days in comparison to those without acid attack at the same age. The density of acid attack increased with increasing MK with a maximum corresponding to 5% MK concrete. The current findings suggest that the local MK has the potential to produce good-quality concrete in a normal environment. [ABSTRACT FROM AUTHOR]

• MASnI 3 , MAGeI 3 and Cs 3 Bi 2 I 9 are considered as eco-friendly perovskite absorber layers for solar cells in this study. • Defect properties in electron transport layer/perovskite interface and perovskite/hole transport layer are studied through numerical simulation. • SCAPS-1D simulation software is used to simulate the solar cells for getting different values of power conversion efficiency by varying the defect density, defect position, the capture cross-section area of electrons and holes in these interfaces. • A clear picture has been achieved regarding the effect of interface defects on solar cell performances. • Investigated outcomes will help the researchers choose the optimum values of the interface defect parameters to get the highest efficiency. Because of the rapid improvement of perovskite (PSK) solar cells (PSCs), they are considered the most promising photovoltaic devices nowadays. This improvement is mainly observed in the lead (Pb)-based PSCs, though Pb is a highly toxic material. Due to this toxicity, Pb-free perovskites have drawn considerable attention as the substitute for Pb-based perovskites. Performances of these Pb-free PSCs depend on the interfaces between the perovskite layer and electron transport layer (ETL) and between the perovskite absorber layer and hole transport layer (HTL). This research focuses on the investigation of these interfaces in different PSCs, in which MASnI 3 , MAGeI 3, and Cs 3 Bi 2 I 9 perovskites are used the absorber layer. The effect of defect density, defect position, and the capture cross-section area of electrons and holes for these interfaces on power conversion efficiency are considered in this study through simulation conducted by the SCAPS-1D simulator. A significant effect of defect density in interfaces has been observed. Deep level traps have been identified as one of the performance degradation reasons. On PCE, the effect of ETL/PSK interface properties is much more significant than the PSK/HTL interface effect. Simulated results would enlighten the researchers to choose the perfect Pb-free perovskite for solar cells that will show greater tolerance against interface defects. Besides, these findings will be helpful in considering the optimum values of interface defect parameters of various types of practical PSCs. [ABSTRACT FROM AUTHOR]

This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R2) for the BR model was 0.95, whereas for SVM and MLP, the R2 was 0.90 and 0.86, respectively. In addition, a k-fold cross-validation approach was adopted to check the accuracy of the employed models. The statistical measures mean absolute percent error, mean absolute error, and root mean square error ensure the validity of the model. Using sensitivity analysis, the influence of input factors on the intended CS of SCC was also explored. This analysis reveals that the highest contributing parameter towards the CS of SCC was cement with 16.2%, while rice husk ash contributed the least with 4.25% among all the input variables. [ABSTRACT FROM AUTHOR]

Interface defects are crucial to improve the perovskite solar cell stability. This study investigates and determines the suitable electron transport layer (ETL) for Sn‐based perovskite solar cells. The investigation finds that carriers capture cross‐sectional area greatly impacts photovoltaic performance, and the optimum value is 1 × 10−25 cm2. A low trap density of 1012 cm−2 does not play a significant role, but an interface defect density of 1017 cm−2 located near the valence band edge at the ETL/absorber interface has the most detrimental effect. In structure, defect states (Et) near the valence band edge of PCBM/CH3NH3SnI3/NiO and WO3/CH3NH3SnI3/NiO devices have a higher destructive impact compared to TiO2/CH3NH3SnI3/NiO. The study also finds that absorber bulk defects and the interface defects in the ETL/absorber layer are the most sensitive to device performance. This detrimental impact on cell performance can be avoided by reducing the capture cross‐sectional area and maintaining a low trap density. [ABSTRACT FROM AUTHOR]

The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (Nc), genes (Ng) and, the head size (Hs) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the Nc = 100, Hs = 8 and Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results. [ABSTRACT FROM AUTHOR]

Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique's higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC's compressive and flexural strengths, respectively. [ABSTRACT FROM AUTHOR]

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data are not endangered after illegal access by hackers and other unauthorized persons. In parallel, these large volumes of confidential industrial data need to be processed within reasonable time for effective deliverables. Currently, there are many mathematical-based symmetric and asymmetric key cryptographic approaches and identity- and attribute-based public key cryptographic approaches that exist to address the abovementioned concerns and limitations such as computational overheads and taking more time for crucial generation as part of the encipherment and decipherment process for large-scale data privacy and security. In addition, the required key for the encipherment and decipherment process may be generated by a third party which may be compromised and lead to man-in-the-middle attacks, brute force attacks, etc. In parallel, there are some other quantum key distribution approaches available to produce keys for the encipherment and decipherment process without the need for a third party. However, there are still some attacks such as photon number splitting attacks and faked state attacks that may be possible with these existing QKD approaches. The primary motivation of our work is to address and avoid such abovementioned existing problems with better and optimal computational overhead for key generation, encipherment, and the decipherment process compared to the existing conventional models. To overcome the existing problems, we proposed a novel dynamic quantum key distribution (QKD) algorithm for critical public infrastructure, which will secure all cyber–physical systems as part of IIoT. In this paper, we used novel multi-state qubit representation to support enhanced dynamic, chaotic quantum key generation with high efficiency and low computational overhead. Our proposed QKD algorithm can create a chaotic set of qubits that act as a part of session-wise dynamic keys used to encipher the IIoT-based large scales of information for secure communication and distribution of sensitive information. [ABSTRACT FROM AUTHOR]

This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks—normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease. [ABSTRACT FROM AUTHOR]

An n -i-p architecture perovskite solar cell (PSC) has been suggested and modelled employing Pb free CH 3 NH 3 SnI 3 absorber layer. A relative investigation for WO 3 (ETL) and Mg-CuCrO 2 (HTL) with different thickness, carrier concentration, interfacial layer (IL) defect tolerance factor, impact of temperature on device performance have been portrayed by SCAPS-1D simulator. A lower thickness of ETL and HTL is quite effective to produce better efficiency. The proposed FTO/WO 3 /CH 3 NH 3 SnI 3 /Mg-CuCrO 2 /Au device shows the maximum V oc , J sc , FF and PCE of 1.02 V, 37.95 mA/cm2, 71.38 %, 27.53 %, respectively. The carrier concentrations N A and N D were optimized at 1x1014 c m - 3 and 1x1016 c m - 3. After those ideals, the projected PSC device's photovoltaic presentation was considerably reduced. The interfacial layer 1 (IL1) and interfacial layer 2 (IL2) exhibited the defect tolerance level of 1x1011 c m - 2 and 1x1016 c m - 2 , respectively. It was found that Au as a back-contact could produce the highest PCE among other back contact metals. The ambient temperature proved to be ideal one to generate the highest PCE since elevated temperatures lead to reduce V oc and PCE thereby. The suggested ETL and HTL have been proved to be alternative inorganic charge transporting materials which could regulate the device performance as well as stability into the environmental atmosphere. [ABSTRACT FROM AUTHOR]

In recent times, the use of fibre-reinforced plastic (FRP) has increased in reinforcing concrete structures. The bond strength of FRP rebars is one of the most significant parameters for characterising the overall efficacy of the concrete structures reinforced with FRP. However, in cases of elevated temperature, the bond of FRP-reinforced concrete can deteriorate depending on a number of factors, including the type of FRP bars used, its diameter, surface form, anchorage length, concrete strength, and cover thickness. Hence, accurate quantification of FRP rebars in concrete is of paramount importance, especially at high temperatures. In this study, an artificial intelligence (AI)-based genetic-expression programming (GEP) method was used to predict the bond strength of FRP rebars in concrete at high temperatures. In order to predict the bond strength, we used failure mode temperature, fibre type, bar surface, bar diameter, anchorage length, compressive strength, and cover-to-diameter ratio as input parameters. The experimental dataset of 146 tests at various elevated temperatures were established for training and validating the model. A total of 70% of the data was used for training the model and remaining 30% was used for validation. Various statistical indices such as correlation coefficient (R), the mean absolute error (MAE), and the root-mean-square error (RMSE) were used to assess the predictive veracity of the GEP model. After the trials, the optimum hyperparameters were 150, 8, and 4 as number of chromosomes, head size and number of genes, respectively. Different genetic factors, such as the number of chromosomes, the size of the head, and the number of genes, were evaluated in eleven separate trials. The results as obtained from the rigorous statistical analysis and parametric study show that the developed GEP model is robust and can predict the bond strength of FRP rebars in concrete under high temperature with reasonable accuracy (i.e., R, RMSE and MAE 0.941, 2.087, and 1.620, and 0.935, 2.370, and 2.046, respectively, for training and validation). More importantly, based on the FRP properties, the model has been translated into traceable mathematical formulation for easy calculations. [ABSTRACT FROM AUTHOR]