The transformative effect of training in counselling and its application, on the community counsellors themselves

Objective: To identify the changes in community counsellors\u27 own level of anxiety and depression as a result of learning counselling skills and to explore their subjective experiences after learning and providing counselling.Design: Quantitative: Repeated Measures. / Qualitative: Focus Group Discussions.SETTING: A lower middle class semi urban community of Karachi, Pakistan.PARTICIPANTS: Twenty-one self selected women from the community.Results: Reduction was seen in the post training scores of anxiety and/or depression in the trainees. As a result of learning and then providing counselling the community counsellors\u27 self esteem, self confidence and sense of competence were enhanced and they developed a more positive attitude towards life.CONCLUSION: A minimal level of training in counselling skills and their application led to significant positive changes in the community counsellors themselves, though self-selection and information bias cannot be ruled out

Spontaneous recovery from depression in women: a qualitative study of vulnerabilities, strengths and resources

Objective: To gain insight into the perceived vulnerability and restitution factors for anxiety/or depression.Methods: Focus group discussion of seven married women recovered spontaneously from anxiety and/or depression, belonging to a lower middle class semi-urban community of Karachi.Results: Poverty, unemployment, abuse and on going difficulties were perceived as risk factors for depression. A reliable social support system, positive thinking approach, faith, prayers, and experiencing a turning point event were reported as factors that promoted recovery from anxiety and/or depression.CONCLUSION: Individual vulnerabilities, strengths and resources can have an important role in recovery from anxiety and/or depression in women

SAM-SoS: A stochastic software architecture modeling and verification approach for complex System-of-Systems

A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any potential impact on the properties critical for achieving the missions. Current modeling approaches lack the essential syntax and semantics required to model and verify SoS behaviors at design time and cannot offer alternative design choices for better design decisions. Therefore, the majority of existing techniques fail to provide qualitative and quantitative verification of SoS architecture models. Consequently, we have proposed an approach to model and verify Non-Deterministic (ND) SoS in advance by extending the current algebraic notations for the formal models as a hybrid stochastic formalism to specify and reason architectural elements with the required semantics. A formal stochastic model is developed using a hybrid approach for architectural descriptions of SoS with behavioral constraints. Through a model-driven approach, stochastic models are then translated into PRISM using formal verification rules. The effectiveness of the approach has been tested with an end-to-end case study design of an emergency response SoS for dealing with a fire situation. Architectural analysis is conducted on the stochastic model, using various qualitative and quantitative measures for SoS missions. Experimental results reveal critical aspects of SoS architecture model that facilitate better achievement of missions and QAs with improved design, using the proposed approach

Prevalence of and factors associated with anxiety and depression among women in a lower middle class semi-urban community of Karachi, Pakistan

Objective: To study the prevalence of, and factors associated with anxiety and depression among women.Design: A cross sectional survey.SETTING: A lower middle class semi-urban community of Karachi, Pakistan.PARTICIPANTS: A total of 1218 women between the ages of 18-50 years.METHODOLOGY: Systematically every third household was identified from which a woman was randomly selected. The Aga Khan University Anxiety and Depression Scale and a socio-demographic questionnaire were administered verbally by trained interviewers for assessing the prevalence of, and associated factors for anxiety and depression.Results: A prevalence of 30% was found. Increasing age, lack of education and verbal abuse were the associated factors found to have an independent relationship.CONCLUSION: Providing education and reducing domestic abuse could lead to decrease in the prevalence of anxiety and depression in women

Heuristic edge server placement in Industrial Internet of Things and cellular networks

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current Information and Communications Technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay is a challenging problem. In this paper, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom’s base station the dataset that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multi-objective constraint optimization problem that places edge servers strategically to the balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base-stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hillclimbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost functio

Loss of function mutations in RP1 are responsible for retinitis pigmentosa in consanguineous familial cases.

PurposeThis study was undertaken to identify causal mutations responsible for autosomal recessive retinitis pigmentosa (arRP) in consanguineous families.MethodsLarge consanguineous families were ascertained from the Punjab province of Pakistan. An ophthalmic examination consisting of a fundus evaluation and electroretinography (ERG) was completed, and small aliquots of blood were collected from all participating individuals. Genomic DNA was extracted from white blood cells, and a genome-wide linkage or a locus-specific exclusion analysis was completed with polymorphic short tandem repeats (STRs). Two-point logarithm of odds (LOD) scores were calculated, and all coding exons and exon-intron boundaries of RP1 were sequenced to identify the causal mutation.ResultsThe ophthalmic examination showed that affected individuals in all families manifest cardinal symptoms of RP. Genome-wide scans localized the disease phenotype to chromosome 8q, a region harboring RP1, a gene previously implicated in the pathogenesis of RP. Sanger sequencing identified a homozygous single base deletion in exon 4: c.3697delT (p.S1233Pfs22*), a single base substitution in intron 3: c.787+1G>A (p.I263Nfs8*), a 2 bp duplication in exon 2: c.551_552dupTA (p.Q185Yfs4*) and an 11,117 bp deletion that removes all three coding exons of RP1. These variations segregated with the disease phenotype within the respective families and were not present in ethnically matched control samples.ConclusionsThese results strongly suggest that these mutations in RP1 are responsible for the retinal phenotype in affected individuals of all four consanguineous families

Effect of various nutrient combinations on growth and body composition of rohu (Labeo rohita)

A total of 80 Labeo rohita fingerlings (mean body weight, 14.7 ± 0.08 g and length, 11.0 ± 0.16 cm) were randomly distributed into four treatments with 20 replicates each, for 60 days, to determine the effect of different feed compositions on the growth and body composition of L. rohita. Four isoenergetic (17.05 ± 0.24 kJ g-1) experimental diet viz., control (C), protein rich (PR), fat rich (FR) and carbohydrate rich (CR) were formulated. The proximate composition protein/fat/carbohydrate (P/F/C) of formulated feed were C: P35/F8/C2, PR: P40/F8/C2, FR: P35/F10/C2 and CR: P35/F8/C5. The daily ration size was 5% of fish body weight. The result reveals a highly significant (P≤0.001) difference in specific growth rate (SGR), weight gain (WG) and protein efficiency (PE) among four feeding groups, while differences were significant for feed conversion ratio (FCR). FR showed maximum growth together with high body fat, CR showed low body fat and high proteins. Results indicate that increasing fat up to 9% in diet showed better growth as compared to increasing dietary protein and carbohydratesKey words: Labeo rohita, diet composition, specific growth rate, protein efficiency, body composition

Environment Friendly Energy Cooperation in Neighboring Buildings : A Transformed Linearization Approach

Funding Information: Funding: This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF-2020R1A2B5B02002478, and in part by the Sejong University Research Faculty Program (20212023). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Peer reviewedPublisher PD

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

© 2023 Tech Science Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model is trained from scratch to determine the classification success rate. 10,000 tomato leaf images (1000 images per class) from the publicly accessible dataset, covering one healthy category and nine disease categories, are utilized in training the proposed DTomatoDNet approach. More specifically, we classified tomato leaf images into Target Spot (TS), Early Blight (EB), Late Blight (LB), Bacterial Spot (BS), Leaf Mold (LM), Tomato Yellow Leaf Curl Virus (YLCV), Septoria Leaf Spot (SLS), Spider Mites (SM), Tomato Mosaic Virus (MV), and Tomato Healthy (H). The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%, demonstrating excellent accuracy in differentiating between tomato diseases. The model could be used on mobile platforms because it is lightweight and designed with fewer layers. Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.Peer reviewe

Fabrication and Characterization of Effective Biochar Biosorbent Derived from Agricultural Waste to Remove Cationic Dyes from Wastewater

The main aim of this work is to treat sugarcane bagasse agricultural waste and prepare an efficient, promising, and eco-friendly adsorbent material. Biochar is an example of such a material, and it is an extremely versatile and eco-friendly biosorbent to treat wastewater. Crystal violet (CV)-dye and methylene blue (MB)-dye species are examples of serious organic pollutants. Herein, biochar was prepared firstly from sugarcane bagasse (SCB), and then a biochar biosorbent was synthesized through pyrolysis and surface activation with NaOH. SEM, TEM, FTIR, Raman, surface area, XRD, and EDX were used to characterize the investigated materials. The reuse of such waste materials is considered eco-friendly in nature. After that, the adsorption of MB and CV-species from synthetically prepared wastewater using treated biochar was investigated under various conditions. To demonstrate the study’s effectiveness, it was attempted to achieve optimum effectiveness at an optimum level by working with time, adsorbent dose, dye concentration, NaCl, pH, and temperature. The number of adsorbed dyes reduced as the dye concentrations increased and marginally decreased with NaCl but increased with the adsorbent dosage, pH, and temperature of the solution increased. Furthermore, it climbed for around 15 min before reaching equilibrium, indicating that all pores were almost full. Under the optimum condition, the removal perecentages of both MB and CV-dyes were ≥98%. The obtained equilibrium data was represented by Langmuir and Freundlich isotherm models. Additionally, the thermodynamic parameters were examined at various temperatures. The results illustrated that the Langmuir isotherm was utilized to explain the experimental adsorption processes with maximum adsorption capacities of MB and CV-dyes were 114.42 and 99.50 mgg$_{−1}$, respectively. The kinetic data were estimated by pseudo-first and pseudo-second-order equations. The best correlation coefficients of the investigated adsorption processes were described by the pseudo-second-order kinetic model. Finally, the data obtained were compared with some works published during the last four years