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The cabbage butterfly (Pieris brassicae L.) is a major insect pest of cabbage in Pakistan. Heterorhabditis bacteriophora (at 1500 infective juvenile/ ml concentraction), Steinerma glaseri (at 1500 infective juvenile/ ml concentration), H. bacteriophora + S. glaseri (at 1500 infective juvenile/ ml concentration), Xenorhabdus spp., (at 4 × 1012 concentration) Photorhabdus spp., (at 4 × 1012 concentration) metabolites of Xenorhabdus spp. (at 50% concentration) and metabolites of Photorhabdus spp (at 50% concentration) were sprayed on cabbage hybrid CB-60 grown in pots placed in the greenhouse. After 7 days, the highest P. brassicae larval mortality (90.43%) was observed where H. bacteriophora + S. glaseri were sprayed. In field experiment, best-performing treatments from greenhouse experiment were selected i.e. H. bacteriophora + S. glaseri (at 1500 infective juvenile/ ml concentration), Xenorhabdus spp., (at 4 × 1012 concentration) and metabolites of Xenorhabdus spp. (at 50% concentration) were sprayed on a highly susceptible cabbage hybrid (CB-60). Heterorhabditis bacteriophora + Steinerma. glaseri @ 1500 infective juvenile/ ml was found to be more superior with maximum mortality (80.43) of cabbage butterfly larvae in comparison with other treatments. From the present study, it may be concluded that EPNs (H. bacteriophora and S. glaseri) application is more effective as compared to their symbiotic bacteria and metabolite for cabbage butterfly management.

Autonomous driving research is an emerging area in the machine learning domain. Most existing methods perform single-task learning, while multi-task learning (MTL) is more efficient due to the leverage of shared information between different tasks. However, MTL is challenging because different tasks may have different significance and varying ranges. In this work, we propose an end-to-end deep learning architecture for statistically correlated MTL using a single input image. Statistical correlation of the tasks is handled by including shared layers in the architecture. Later network separates into different branches to handle the difference in the behavior of each task. Training a multi-task model with varying ranges may converge the objective function only with larger values. To this end, we explore different normalization schemes and empirically observe that the inverse validation-loss weighted scheme has best performed. In addition to estimating steering angle, braking, and acceleration, we also estimate the number of lanes on the left and the right side of the vehicle. To the best of our knowledge, we are the first to propose an end-to-end deep learning architecture to estimate this type of lane information. The proposed approach is evaluated on four publicly available datasets including Comma.ai, Udacity, Berkeley Deep Drive, and Sully Chen. We also propose a synthetic dataset GTA-V for autonomous driving research. Our experiments demonstrate the superior performance of the proposed approach compared to the current state-of-the-art methods. The GTA-V dataset and the lane annotations on the four existing datasets will be made publicly available via https://cvlab.lums.edu.pk/scmtl/.

Cabbage butterfly (Pieris brassicae) is major pest of cabbage. More than 50% yield losses have been attributed due to this pest annually. Seven insect parasitic nematodes i.e. Steinerma carpocapsae, S. feltiae, S. Pakitananese, S. asiaticum, S. glaseri, Heterorhabditis bacteriophora and H. indica were evaluated on 4th larval instar of P. brassicae at the different concentrations. Two best performing EPNs (S. glaseri and H. bacteriophora) were selected and evaluated against 2nd, 3rd and 4th larval instars of Pieris brassicae at 1500 infective juveniles/ml. H. bacteriophora spp. and S. glaseri spp. were sprayed at 1500 IJs/ml on the cabbage leaves alone or in combination (S. glaseri + H. bacteriophora) and 2nd, 3rd and 4th larval instars were fed on them. Among seven EPNs, hundred percent mortality of P. brassicae was recorded in the case of H. bacteriophora and S. glaseri at1500 IJs/ml concentration after 48 h. Both EPNs spp. were found highly effective against all larval instars of P. brassicae. After 48 h of exposure of both EPNs spp. on all larval instar, 100% mortality was recorded. The combined application of H. bacteriophora + S. glaseri on cabbage leaves resulted into 100% mortality of all larval instars of P. brassicae. From present study, it may be concluded that H. bacteriophora and S. glaseri at 1500 IJs/ml concentration found very effective against P. brassicae in laboratory conditions and may be used in the field conditions.

A series of Cu-Co-substituted manganese ferrites (Mn1-xCuxFe2-yCoyO4; x = 0.0–0.1, y = 0.00–0.25) were synthesized via microemulsion method. The values of structural parameters, i.e., lattice constant and crystallite size, were increased with the inclusion of Co and Cu ions. FTIR studies revealed first band at 400–500 cm−1 and second band at 561–588 cm−1. The values of dielectric parameters have been increased with the increase of Cu and Co concentrations. The magnetic parameters, i.e., saturation magnetization (Ms), coercivity (Hc), and remanence (Mr), have been investigated from M-H loops. The values of Ms and Mr were observed to increase from 5.00 to 36.02 emu/g and from 4.6 to 13.2 emu/g, respectively. The value of coercivity had been decreased from 466 to 261 Oe. The increase in saturation magnetization as well as decrease in dielectric losses proposed that synthesized nanoferrites may be potential candidates for high frequency and microwave applications.

Adversaries may cause significant damage to smart infrastructure using malicious attacks. To detect and mitigate these attacks before they can cause physical damage, operators can deploy anomaly detection systems (ADS), which can alarm operators to suspicious activities. However, detection thresholds of ADS need to be configured properly, as an oversensitive detector raises a prohibitively large number of false alarms, while an undersensitive detector may miss actual attacks. This is an especially challenging problem in dynamical environments, where the impact of attacks may significantly vary over time. Using a game-theoretic approach, we formulate the problem of computing optimal detection thresholds which minimize both the number of false alarms and the probability of missing actual attacks as a two-player Stackelberg security game. We provide an efficient dynamic programming-based algorithm for solving the game, thereby finding optimal detection thresholds. We analyze the performance of the proposed algorithm and show that its running time scales polynomially as the length of the time horizon of interest increases. In addition, we study the problem of finding optimal thresholds in the presence of both random faults and attacks. Finally, we evaluate our result using a case study of contamination attacks in water networks, and show that our optimal thresholds significantly outperform fixed thresholds that do not consider that the environment is dynamical.

Nanoparticles (NPs) of barium hexaferrite were synthesized by a chemical co-precipitation technique in an aqueous solution with a nominal composition of BaFe11(Sn0.5Mg0.5)1−xNdxO19 (x = 0, 0.5, 1). XRD and FTIR analyses were performed to study the crystal structure of the magnetic powders (MPs) in order to obtain details on the hexaferrite structure. Grain morphology and elemental composition were assessed by SEM and EDS, respectively, and nano-sized hexagonal platelet-like structures in all MPs were demonstrated. All of the structural parameters such as “a”, “c”, V(cell) and Dx have been altered with successful incorporation of Nd into hexagonal lattices, thereby the band gap Eg first increased x ≤ 0.5 then decreased up to x = 1 (i.e., drawn via a Tauc plot) from 1.53–1.82 eV. The measurements of dielectric properties depict relaxation behavior at higher frequencies. The dielectric measurements increase with increased hexaferrite. Furthermore, the magnetic properties of all MPs were examined by VSM at room temperature (RT). The saturation magnetization (Ms), remnant magnetization (Mr), and coercivity (Hc) were linearly decreased by the SnMg content, whereas an increasing trend appeared with the additional Nd content. However, the squareness ratio increased with an increase in the content (x). These outcomes make this substitution an excellent candidate for multiple applications such as optical, electrical-based devices, circulators, and recording materials.

The main purpose of research was to investigate the underlying strategies for enhancing “organizational sustainability” (OS). The study categorizes the existing literature, based on strategic choice theory and the attribution model, which reveals that green human resource management (GHRM) practices play a crucial role in environmental management and organizational sustainability. This study investigates the impact of GHRM practices including training and development, performance appraisal, and reward and compensation on organizational sustainability through the mediating role of environmental performance and employee performance. A survey questionnaire methodology was used for data collection; data were collected from 165 managerial personnel in the textile industrial sector of Pakistan. The findings of the study showed significant effects of GHRM practices, i.e. (training and development, performance appraisal, and reward and compensation), on Organizational Sustainability, Similarly, this study empirically investigates the distinct mediating role of environmental performance and employee’s performance between GHRM practices and organizational sustainability. The study findings support the hypothesized model of mediation. The GHRM is an innovative idea in developing countries, and additional studies are needed to identify sustainability issues and evaluate the impacts of GHRM practices in the textile and manufacturing industries in Pakistan.

A cognitive radio network has a high promise to improve spectrum utilization, through exploitation of spectrum holes from the primary network. However, in a distributed environment, employing contention based random access, collisions and unattempted slots result in waste of the discovered holes. This waste is even costlier for a CRN as resources are consumed in finding the holes. In this paper, we have proposed a self-organizing distributed CR-MAC protocol so that the contention is not required for medium access, thereby, minimizing the waste of opportunity due to collisions or for being left idle in random access, consequently, improving the spectral efficiency. This is achieved by organizing and queuing the active secondary users through a timer value and directing them in an orderly fashion to use the discovered holes. The proposed scheme successfully assigns a channel to more than 99% of the secondary users in less than 5% of the idle slots from the primary network, compared to that required in contention based random access schemes.

The research was conducted to evaluate in-vitro efficacy of numerous fungicides against Fusarium oxysporum f. sp. capsici. In present research, six treatments (T) viz. Carbendazim, Benomyl, Topsin-M, Difenoconazole, Nativo, and Alliete along with control, various concentrations (C), days (D), and their interactions, i.e., (T × C), (T × D), (C × D), and (T × C × D) were exploited in a laboratory through food poison technique. Alliete expressed maximum colony growth (1.93 cm) as compared to all other fungicides with respect to control. Interaction between treatments and concentration (T × C) exhibited maximum colony growth of all treatments (Carbendazim, Benomyl, Topsin-M, Difenoconazole, Nativo, and Alliete), i.e., 0.87, 1.23, 1.73, 2.20, 2.53, and 2.93 cm at 300 ppm as compared to 500 and 700 ppm concentrations, respectively. Similar trend was also observed concerning interaction between (fungicides × days) and (tested concentrations × days). Results of the present study revealed that among tested fungicides, Carbendazim at 700 ppm expressed significant reduction in fungal growth.

WOS: 000473543500005, The study demonstrates an efficient, simple and on-site viable approach for sensitive determination of blood glucose using colorimetric sensing approach. The devised colorimetric sensor works based on cysteine functionalized silver nanoparticles. The as-synthesized Ag NPs were elaborately characterized using advanced analytical techniques such as ultra-violet visible spectroscopy, Fourier transform infrared spectroscopy, transmission electron microscopy, atomic force microscopy and X-ray diffraction. The detection of glucose was carried under ambient air conditions where change in the optical characteristics of Ag NPs and subsequent interaction between glucose and functional moiety (i.e. surface bind cysteine) of Ag NPs was considered as the signal response. This interaction led to direct detection of glucose in the concentration range between 0.01 to 0.17 mu M with limit of detection up to 1 x 10(-4) mu M. It is worthwhile mentioning that success of the assay lies in application of the developed colorimetric sensor in real blood glucose measurements, which also proved its capability for field based analysis. In addition its simple design, low cost, and more reliability signifies the usefulness of colorimetric sensor and it can be extended monitor other biologically important molecules., Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [1059B211600383], This study was funded by The Scientific and Technological Research Council of Turkey (TUBITAK) under 2221-Fellowships for Visiting Scientists and Scientists on Sabbatical Leave, the Grant Number of the project is 1059B211600383.

The treatment of organic pollution via photocatalysis has been investigated for a few decades. However, earth-abundant, cheap, stable, and efficient substrates are still to be developed. Here, we prepare an efficient visible-light-driven photocatalyst via the deposition of Ag nanoparticles (

Automatic recognition of isolated spoken digits is one of the most challenging tasks in the area of Automatic Speech Recognition. In this paper, Database Development and Automatic Speech Recognition of Isolated Pashto Spoken Digits from Sefer (0) to Naha (9) has been presented. A number of 50 individual Pashto native speakers (25 male and 25 female) of different ages, ranging from 18 to 60 years, were involved to utter from Sefer (0) to Naha (9) digits separately. Sony PCM-M 10 linear recorder is used for recoding purpose in the office and home in noise free environment. Adobe audition version 1.0 is used to split the audio of digits into individual digits and result is saved in .wav format. Mel frequency cepstral coefficients is used to extract speech features. K nearest neighbor classifier is used for the first time up to author knowledge in Pashto language to classify the features of speech and compare its accuracy with linear discriminate analysis. The experimental results are evaluated, and the overall average recognition exactitude of 76.8 % is obtained.