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Multilevel inverters have gained importance in modern power systems during the last few years because of their high power quality with lower THD. Various topologies developed include the packed U-cell inverter and its different modified versions that have emerged as a compact and efficient solution to distributed energy systems. Most of the available harmonic mitigation techniques, that is, passive filtering and individual optimization techniques, which include GA and PSO, are susceptible to a variety of shortcomings regarding their inherent complexity and inefficiency; hence, finding an appropriate convergence may be quite hard. This paper proposes a hybrid version of the GA-PSO algorithm that exploits the exploratory strengths of GA and the convergence efficiencies of PSO in determining the optimized switching angles for SHM techniques applied to modified five-level and seven-level PUC inverters. By utilizing the multi-objective optimization method, the approach minimizes THD while keeping voltage and efficiency constraints. Simulated in MATLAB/Simulink, the results were experimentally verified using hardware-in-the-loop testing on OP5700. A large THD reduction in both MPUC7 (11.68%) and MPUC5 (17.61%) was obtained. The proposed hybrid algorithm outperformed the standalone approaches of GA and PSO with respect to robustness and with precise harmonic suppression. Other appealing features are reduced computational complexity and improved waveform quality; hence, the method is highly suitable for both grid-tied and standalone renewable energy applications. This work lays a basis for efficient inverter designs that can adapt well under dynamic load conditions.

A single-phase multilevel inverter with a switched-capacitor multilevel (SC-MLI) configuration is developed to provide 13-level output voltages. An improved genetic algorithm (GA) with adaptive mutation and crossover rates is employed to achieve robust harmonic mitigation by avoiding local optima and ensuring optimal performance. The topology introduces an SC-MLI that generates AC output voltage at desired levels using only two capacitors, two asymmetrical DC sources, one diode, and 11 switches. This allows the inverter to use fewer gate drivers and, hence, increases the power density of the converter. A significant challenge in the normal operation of SC-MLI circuits relates to the self-voltage balance of the capacitors, which easily becomes unstable, particularly at low modulation indices. The proposed design addresses this issue without the need for ancillary devices or complex control schemes, ensuring stable self-balanced operation across the entire spectrum of the modulation index. In this context, the harmonic mitigation technique optimized through GA applied in this inverter ensures low harmonic distortion, achieving a total harmonic distortion (THD) of 6.73%, thereby enhancing power quality even at low modulation indices. The performance of this SC-MLI is modeled under various loading scenarios using MATLAB/Simulink® 2023b with validation performed through an Opal-RT real-time emulator. Additionally, the inverter’s overall power losses and individual switch losses, along with the efficiency, are analyzed using the simulation tool PLEXIM-PLECS. Efficiency is found to be 96.62%.

Heavy metal (HM) contamination in the drinking water is a serious threat to the consumers and has drawn the global attention. In the current study, twenty six (26) groundwater samples including tube well and domestic bores were collected randomly from fourteen union councils (UCs) of tehsil Swabi. Depth of the tube wells ranged between 100 and 400 feet, while in domestic bore water, it ranged from 22 to 140 feet. Samples were analyzed for different parameters. Concentrations of the heavy metals (HMs) were found in the following increasing orders of Pb > Zn > Cr > Cu > Ni > Cd > Co in domestic well water while in tube well water, the increasing order was Cu > Ni > Co > Zn > Cr > Cd. In the domestic well samples, Cd, Cr, Ni, and Pb were found above the World health organization (WHO), permissible limits. Based on the Water Quality Index (WQI), samples collected from domestic wells were found of poor quality with WQI value of 208, while tube well waters were found of excellent quality, with WQI value of 40. This indicates that domestic well waters are more prone to HM contamination due to low depth. Health risk data showed significantly high risk (HQ > 1) for children upon domestic well water consumption, due to high concentrations of Cr and Pb. No significance relationship was observed between the various parameters which indicate the input of these contaminants from multiple sources.

Introduction This qualitative study explored factors associated with childcare affecting antibiotic use in a lower socioeconomic, culturally diverse community. Little is known about the knowledge, attitudes and behaviours of parents and childcare providers that influence childhood antibiotic use at a local level. Methods Parents and childcare workers from the Fairfield Local Government Area of Sydney were sampled to maximise diversity, including English speakers and those preferring other languages. Recruitment was hampered by the COVID-19 epidemic. Semi-structured telephone interview transcripts were thematically analysed. Results Eighteen childcare staff and 20 parents were interviewed, drawn from 7 participating childcare services. Interview findings were grouped under two major themes: the culture regarding antibiotic use and the regulatory and administrative environment of childcare centres. Interview responses demonstrated interaction between themes and provided insights into the knowledge, attitudes, and behaviours of staff and parents/carers in relation to antibiotic use. Discussion The determinants of high use of antibiotics in childcare in a multicultural community are multifactorial, inter-related and complex. The two interacting themes, cultural factors and regulatory/administrative environment, appear to capture these determinants. The study did not find evidence of explicit pressure on parents to obtain antibiotics for children. However, the themes described appear to work together to increase antibiotic prescriptions. Parents and care providers expressed beliefs in antibiotic efficacy for numerous conditions, contrary to scientific knowledge and public health messaging. Respondents were not aware that antibiotic use in the region is unusually high. The regulatory and administrative context determining childcare attendance during illness does not seem to overtly drive antibiotic seeking behaviour. However, parents expressed an imperative to work which appeared to drive adoption of strategies perceived to shorten illness, including using antibiotics. These factors may also increase doctor attendance seeking certificates to facilitate early return to childcare. Doctor attendance may increase antibiotic prescriptions. These issues deserve further investigation which should also include doctors’ perspectives.

The integration of Distributed Energy Resources (DERs) such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) introduces new challenges to distribution networks despite offering opportunities for decarbonization and grid flexibility. This paper proposes a scalable simulation-based framework that combines deterministic nodal hosting capacity analysis with probabilistic Monte Carlo simulations to evaluate and optimize DER integration in diverse feeder types. The methodology is demonstrated using the IEEE 13-bus and 123-bus test systems under full-year time-series simulations. Deterministic hosting capacity analysis shows that individual nodes can accommodate up to 76% of base load from PV sources, while Monte Carlo analysis reveals a network-wide average hosting capacity of 62%. Uncoordinated DER deployment leads to increased system losses, overvoltages, and thermal overloads. In contrast, coordinated integration achieves up to 38.7% reduction in power losses, 25% peak demand shaving, and voltage improvements from 0.928 p.u. to 0.971 p.u. Additionally, congestion-centric optimization reduces thermal overload indices by up to 65%. This framework aids utilities and policymakers in making informed decisions on DER planning by capturing both spatial and stochastic constraints. Its modular design allows for flexible adaptation across feeder scales and configurations. The results highlight the need for node-specific deployment strategies and probabilistic validation to ensure reliable, efficient DER integration. Future work will incorporate optimization-driven control and hardware-in-the-loop testing to support real-time implementation.

Emotion recognition in speech is essential for enhancing human–computer interaction (HCI) systems. Despite progress in Bangla speech emotion recognition, challenges remain, including low accuracy, speaker dependency, and poor generalization across emotional expressions. Previous approaches often rely on traditional machine learning or basic deep learning models, struggling with robustness and accuracy in noisy or varied data. In this study, we propose a novel multi-stream deep learning feature fusion approach for Bangla speech emotion recognition, addressing the limitations of existing methods. Our approach begins with various data augmentation techniques applied to the training dataset, enhancing the model’s robustness and generalization. We then extract a comprehensive set of handcrafted features, including Zero-Crossing Rate (ZCR), chromagram, spectral centroid, spectral roll-off, spectral contrast, spectral flatness, Mel-Frequency Cepstral Coefficients (MFCCs), Root Mean Square (RMS) energy, and Mel-spectrogram. Although these features are used as 1D numerical vectors, some of them are computed from time–frequency representations (e.g., chromagram, Mel-spectrogram) that can themselves be depicted as images, which is conceptually close to imaging-based analysis. These features capture key characteristics of the speech signal, providing valuable insights into the emotional content. Sequentially, we utilize a multi-stream deep learning architecture to automatically learn complex, hierarchical representations of the speech signal. This architecture consists of three distinct streams: the first stream uses 1D convolutional neural networks (1D CNNs), the second integrates 1D CNN with Long Short-Term Memory (LSTM), and the third combines 1D CNNs with bidirectional LSTM (Bi-LSTM). These models capture intricate emotional nuances that handcrafted features alone may not fully represent. For each of these models, we generate predicted scores and then employ ensemble learning with a soft voting technique to produce the final prediction. This fusion of handcrafted features, deep learning-derived features, and ensemble voting enhances the accuracy and robustness of emotion identification across multiple datasets. Our method demonstrates the effectiveness of combining various learning models to improve emotion recognition in Bangla speech, providing a more comprehensive solution compared with existing methods. We utilize three primary datasets—SUBESCO, BanglaSER, and a merged version of both—as well as two external datasets, RAVDESS and EMODB, to assess the performance of our models. Our method achieves impressive results with accuracies of 92.90%, 85.20%, 90.63%, 67.71%, and 69.25% for the SUBESCO, BanglaSER, merged SUBESCO and BanglaSER, RAVDESS, and EMODB datasets, respectively. These results demonstrate the effectiveness of combining handcrafted features with deep learning-based features through ensemble learning for robust emotion recognition in Bangla speech.

 To compete in the international market, the country with a competitive advantage in the heavy manufacturing industry should increase their logistics efficiency. This paper aims to contribute to the optimisation model for project logistics and proposes the improvement strategy for multimodal transport, especially for project logistics cargo. Mixed-method research consisted of a qualitative study to observe the main criteria for transport analysis and perform a case analysis. In the study case about multimodal transport analysis for exporting 250 units of railway carriages from Indonesia's manufacturer to Chittagong Port, the optimum cost obtained based on a combination of the single -12 multi axle-line trailer and General Cargo Vessel 28000 DWT with the unit cost US$16,700 per carriages and total shipping duration of 142 days. The multimodal strategies are more complex than unimodal transport that involve suppliers, management, logistics function improvement, and lean transport management adoption.

Ports are an essential infrastructure for global trade, with over 4000 seaports operating worldwide. As international trade continues to grow at a rapid pace, the development of new ports is increasing at a similar rate. During the construction and operating phase, ports are highly intersected with environmental effects, economic growth, and social impact on their surroundings. However, with the increasing pressure to mitigate their adverse climate and environmental impact, implementing sustainability frameworks offers a promising path forward. This paper studied the potential benefits of incorporating sustainability frameworks into port operation and development, focusing on environmental protection, economic sustainability, and social effects. The paper presented case studies showing the successful implementation of port sustainability frameworks, offering practical insights and best practices that can be directly applied by port authorities, legislators, and stakeholders engaged in port planning and operations.

Objective: The experiment was designed to establish suitable management strategies through the different feeding and breeding approaches on fertility improvement in the experimental repeat breeding (RB) cows. Materials and Methods: 80 RB cows were selected for this experiment. Before grouping, all cows were deworming and then divided into four equal groups, namely Group-TF1 [traditional feeding practice and natural insemination (NI)], Group-TF2 [traditional feeding practice and Artificial insemination (AI)], Group-SF1 [standard (STD) feeding practice and NI], and Group-SF2 (STD feeding practice and AI). These allocated RB cows were fed by traditional and STD feeding methods for 90 days and then inseminated by AI and NI breeding systems. The dominant follicle (DF) diameter, hemato-biochemical elements, and estrogen (E2) hormone were estimated during the insemination of cows. Estimation of the pregnancy rate was carried out at days 45-90 post-insemination in the cows. Results: The pregnancy rate was significantly (p < 0.05) higher in STD feeding practice with NI when compared to traditional feeding practice irrespective of breeding systems, and it was also significantly (p < 0.05) higher in NI than in AI breeding system, irrespective of feeding strategies. The results also showed that the diameter of DF, serum E2, total erythrocyte count, hemoglobin, packed cell volume, total cholesterol, total protein, glucose, calcium, phosphorus, ferric iron, copper, zinc, and magnesium at the time of insemination were significantly (p < 0.01) elevated in the experimental RB cows with STD feeding practice. The diameter of DF and serum E2 were significant (p < 0.01) and positively correlated with all hemato-biochemical elements in the cows at the time of insemination. Conclusion: The results suggest that NI with STD feeding practice may increase fertility in RB cows by improving general health status. Finally, it could support the veterinarians and researchers to define the management strategies using feeding and breeding strategies to prevent repeat breeding syndrome in dairy cows.

The objective of this research work is to know the productive and reproductive performances and problems of local, crossbred, Nilli, and Murrah buffalo cows in selected study areas in Bangladesh. A total of 1,241 local, crossbred, Nilli, and Murrah buffalo cows were surveyed in the selected areas with a pre-set questionnaire. Among 1,241 buffalo cows, 112 buffalo cows were randomly selected at day 0 of the estrus cycle for studying ovarian features. Results showed that the average age, body condition score, and body weight were significantly ( < 0.05) different among the studied breeds. Milk production in Murrah and lactation length in Nilli cows were significantly ( < 0.05) higher than indigenous, crossbred, Nilli, and indigenous, crossbred, Murrah buffalo cows, respectively. Results also illustrated that sexual maturity, estrus cycle length, insemination time after the onset of estrus, and gestation length insignificantly ( > 0.05) varied among the surveyed breed. But, the fallout of the study denoted that estrus duration, first calving age, parity number, number of service per conception, calving interval, and voluntary waiting period varied significantly ( < 0.05) in different breeds. Ovarian physiological characteristics such as vaginal electrical resistance, average number of follicles in two ovaries, and largest follicular diameter, estrogen, and progesterone at day 0 of the estrus cycle of local, crossbred, Nilli, and Murrah buffalo cows showed insignificantly ( > 0.05) differences. The study will help the veterinarian and researcher to identify the constraints for the reproductive efficiency of buffalo in Bangladesh.