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A reconfigurable manufacturing system (RMS) is one of the next generation production systems widely used to meet uncertain market demands in the context of Industry 4.0. The design of the RMS aims to achieve sufficient responsiveness so that it can be quickly adopted to the changes required for a niche market of a customized product family. Most components of the RMS are designed to be modular. Reconfigurable machines are one of the main modular components of the RMS. In the design of the RMS, the problem of machine selection is of primary interest, as the modular machines, with their respective tools and configurations, are selected to perform a given part from the part family. Due to this trilogy of machine, tool, and configuration selection, only one type of machine is considered. To remedy this shortcoming, this work introduces a new concept of modular machine configuration capability, which leads to the selection of two types of machines, namely, the single-spindle modular reconfigurable machines (SRMT) and the multi-spindle reconfigurable machines (MRMT). This paper addresses the problem of machine selection and RMS design. Firstly, a bi-objective mathematical model is developed for the generation of the process plan and the selection of reconfigurable machines. The results obtained, together with an initial layout, are then used to generate the RMS design. Secondly, a new objective function is introduced to address the problem of under-utilization of reconfigurable multi-spindle machines. A NSGA-III (non-dominating sorting genetic algorithm III)-based approach is proposed to solve the proposed models. To help the decision maker, the pseudo-weight technique is used to determine the best process plan and the best machines to include in the new designed RMS.

Limited literature is available on the dietary pattern and its consequences on health of women living in coastal slums of Karachi, Pakistan. The study employed a mixed-method approach where concurrent quantitative and qualitative assessments were carried out. An analytical cross-sectional survey was conducted to collect information on demographic, household, obstetrics characteristics, and dietary pattern of married women of reproductive age (MWRA). Blood samples were collected to identify the hemoglobin level to determine anemia. For the qualitative component, focus group discussions were carried out with women and in-depth interviews with shopkeepers to understand the availability of food items at household level and in local markets respectively. In addition, observational visits were carried out at different points in time to the local market to document the availability of iron-rich foods for the community. The overall prevalence of anemia in sample population was 68.0%. Women with no formal education (AOR: 2.93 95% CI: 1.90-4.52), who consumed gutka (AOR: 2.84 95% CI: 1.81-4.46), did not eat red meat (AOR: 1.68 95% CI: 1.06-2.65), and only had seafood (AOR: 4.56 95% CI: 1.38-15.02) were more likely to be anemic as compared to their counterparts. Qualitative data revealed that any kind of meat and fruits were beyond the reach of community people due to non-affordable cost. A high percentage of women used a locally produced recreational substance known as gutka which gives them a feeling of wellbeing and suppresses hunger. In our study population, lack of access to diversity of food items, illiteracy, and use of gutka are the statistically significant factors which are associated with anemia in married women of this coastal slum area. The lack of demand for diversity in food is related to poverty and preference of spending money on gutka.

Objectives The exact etiology of Parsonage–Turner syndrome is unknown, but it is known to be preceded by infection, vaccination, or surgical intervention. In this review, we describe associations of Parsonage–Turner syndrome with COVID-19 infection and vaccination. Methods A systematic literature search was conducted using PubMed/MEDLINE, ScienceDirect, and Google Scholar. Microsoft Excel was used for data extraction and statistical analysis. The quality of case reports and case series was assessed using the Joanna Briggs Institute Critical Appraisal Tool. Results We selected 44 case reports and 10 case series, including 68 patients (32 post-vaccination and 36 with post-COVID-19 infection Parsonage–Turner syndrome). Middle-aged males were predominantly affected in both groups. The most frequently administered vaccine was Comirnaty (Pfizer) (53%). The mean latency was 11.7 days in the post-vaccination group and 20.3 days in the post-infection group. The most affected nerves in both groups were the axillary, suprascapular, and musculocutaneous nerves; and 78.1% and 38.9% of patients showed partial amelioration of their symptoms in the post-vaccination and post-infection groups, respectively. Conclusion Post-vaccination Parsonage–Turner syndrome presents earlier than post-infection disease. Pain and sensorimotor deficits of the upper limb are common in both situations. Complete or partial recovery occurs in most cases.

The Burgers–Huxley equation is a nonlinear partial differential equation that incorporates convective, diffusive and reactive effects and arises in various reaction–diffusion and fluid flow models. In this paper, a numerical method based on the method of lines is proposed for its solution. The spatial derivatives are approximated using a third-order finite difference scheme, which converts the governing partial differential equation into a system of ordinary differential equations. The resulting semi-discrete system is solved in time using the classical fourth-order Runge–Kutta method. The stability and convergence properties of the proposed scheme are analyzed to establish its numerical reliability. Several numerical experiments are presented to illustrate the accuracy and efficiency of the method. The computed results confirm that the proposed approach provides accurate and stable solutions for the Burgers–Huxley equation.

Traditional fermented foods are appreciated worldwide for their cultural significance and health-promoting properties. However, traditional fermentation production suffers from many obstacles such as microbial variability, varying quality, and lack of scalability. The implementation of smart fermentation technologies, including biosensors, the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), hold the key to the optimization of microbial process control, enhance product consistency, and improve production efficiency. This review summarizes modern developments in real-time microbial monitoring, IoT, AI, and ML tailored to traditional fermented foods. Despite significant technical advancements, challenges related to high costs, the absence of standardized frameworks, and access restrictions for small producers remain substantial limitations. This review proposed a future direction prioritizing modular, scalable solutions, open-source innovation, and environmental sustainability. In alignment with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure), smart fermentation technologies advance sustainable industry through innovation and serve as a critical bridge between traditional craftsmanship and Industry 4.0, fostering inclusive development while preserving microbial biodiversity and cultural heritage.

This study aims to examine the impact of political instability, food prices, and the crime rate on tourism development in Pakistan over the semi-annual data from 1995 to 2019. To achieve the goal of this study, an asymmetric ARDL technique was used. The Asymmetric Autoregressive Distributed Lag Model (ARDL) aided in gaining access to both positive and negative shocks in political stability, crime rate, and food inflation. The findings showed that due to positive variations in the political conditions, tourism will increase by 0.12%, and if political instability prevails in the country, tourism will decrease by 23%. On the other hand, the magnitude of political stability is less than the negative variation of political instability on tourism. The study concludes that there is a considerable asymmetric association between political instability, crime rate, food prices, and tourism development in Pakistan. Based on these findings, it is advised that the government adopt proactive measures to establish and reinforce the political stability mechanism and terrorism control, as well as to improve the living standards of the general population. Moreover, establish a structure for adaptation efforts, focusing on the coordination of tourism expansion platforms for sustainable tourism in Pakistan to attract more foreigners for the sake of a surge in tourism proceeds.

High Voltage Direct Current (HVDC) systems offer an efficient solution for long-distance power transmission and the integration of remote renewable energy sources. However, protection of the HVDC transmission line poses a significant challenge due to its extremely high short circuit currents and absence of zero crossings. DC faults must be resolved within milliseconds to prevent HVDC grid failure. Thus, advanced protection techniques are crucial for ensuring the efficiency and stability of multi-terminal Voltage Source Converter (VSC) based HVDC systems. This paper proposes a robust and adaptive non-unit protection algorithm for a multi-terminal HVDC grid transmission line, addressing DC fault type discrimination and fault identification under high-impedance fault (HIF) conditions. The algorithm is designed based on distinctive high-frequency components present in the line and pole mode voltage traveling waves (TWs) at various relay units. Daubechies wavelet transform was chosen as the time-frequency tool. The proposed protection scheme accurately detect fault and distinguishes between external and internal DC faults within one millisecond and ensures proper operation during HIF with a 25 dB level of noise interference. A four-terminal meshed HVDC grid is modeled in EMTDC/PSCAD and tested under the positive pole-to-ground (PTG), pole-to-pole (PTP), and negative pole-to-ground (NTG) fault at various locations and times to evaluate the effectiveness of the proposed protection scheme. By exploiting the properties of traveling waves, the algorithm improves the accuracy of DC fault protection, making it suitable for modern HVDC applications. The results indicate that the proposed protection algorithm offers enhanced sensitivity and improved selection performance.

PurposeThe aim of the paper is to determine the asymmetric impact of tourism on the deficit in the balance of payments (BOPs).Design/methodology/approachThe research uses the non-linear autoregressive distributed lag (ARDL) model to investigate the asymmetric impact of tourism on Pakistan's BOPs deficit using quarterly data from 1995 to 2019.FindingsThe finding reveals that due to the positive change in tourism, the BOPs deficit decreases by 27%, although due to the negative change in tourism, the BOPs deficit rises by 2.3%. In addition, the significance of F-statistics (10.609) confirms the existence of co-integration between tourism and the deficit in the BOPs. The Wald test confirms the asymmetric association between tourism and the deficit in the BOPs over the long term.Research limitations/implicationsIn order to improve tourism in Pakistan, policymakers must consider the following implications. First, there is a need for an adequate infrastructure that can help the tourist. Second, the Government must maintain a stable law and order situation as a whole and particularly at tourist destinations. Finally, the Government should develop tourism-friendly policies in order to boost tourism in Pakistan.Originality/valueThe research provides new evidence of the impact of tourism on the BOPs using the novel non-linear ARDL (NARDL) technique. The evidence will help policymakers to develop policies to improve tourism in order to reduce the BOPs deficit.

Neonatal deaths and stillbirths are significant public health concerns in Pakistan, with an estimated stillbirth rate of 43 per 1,000 births and a neonatal mortality rate of 46 deaths per 1,000 live births. Limited access to obstetric care, poor health seeking behaviors and lack of quality healthcare are the leading root causes for stillbirths and neonatal deaths. Rehri Goth, a coastal slum in Karachi, faces even greater challenges due to extreme poverty, and inadequate infrastructure. This study aims to investigate the causes and pathways leading to stillbirths and neonatal deaths in Rehri Goth to develop effective maternal and child health interventions. A mixed-method cohort study was nested with the implementation of large maternal, neonatal and child health program, captured all stillbirths and neonatal death during the period of May 2014 till June 2018. The Verbal and Social Autopsy (VASA) tool (WHO 2016) was used to collect primary data from all death events to determine the causes as well as the pathways. Interviews were conducted both retrospectively and prospectively with mothers and caregivers. Two trained physicians reviewed the VASA form and the medical records (if available) and coded the cause of death blinded to each other. Descriptive analysis was used to categorize stillbirth and neonatal mortality data into high- and low-mortality clusters, followed by chi-square tests to explore associations between categories, and concluded with a qualitative analysis. Out of 421 events captured, complete VASA interviews were conducted for 317 cases. The leading causes of antepartum stillbirths were pregnancy-induced hypertension (22.4%) and maternal infections (13.4%), while obstructed labor was the primary cause of intrapartum stillbirths (38.3%). Neonatal deaths were primarily caused by perinatal asphyxia (36.1%) and preterm birth complications (27.8%). The qualitative analysis on a subset of 40 death events showed that health system (62.5%) and community factors (37.5%) contributing to adverse outcomes, such as delayed referrals, poor triage systems, suboptimal quality of care, and delayed care-seeking behaviors. The study provides an opportunity to understand the causes of stillbirths and neonatal deaths in one of the impoverished slums of Karachi. The data segregation by clusters as well as triangulation with qualitative analysis highlight the needs of evidence-based strategies for maternal and child health interventions in disadvantaged communities.

Climate change and injudicious nitrogen addition alter the soil physico-chemical properties and microbial activity in oligotrophic forest soil, which disrupts the nitrogen cycle balance. Nevertheless, recommended fertilizer forms and levels are considered to be crucial for stable nitrogen application. We established a short-term field trial for the first time using a randomized complete block design under the yellow larch forest, with six treatments applied, including urea CO(NH2)2, ammonium chloride NH4Cl, and sodium nitrate NaNO3 at concentrations of 10 and 20 kg N hm−2 yr−1, each extended by three replicates. The gene abundances were measured using quantitative PCR (qPCR), in which the abundance levels of AOA (amoA) and nirS were higher under high CO(NH2)2 2.87 × 1010 copies g−1 dry soil and low NO3− 8.82 × 109 copies g−1 dry soil, compared to CK, representing 2.8-fold and 1.5-fold increases, respectively. We found niche partitioning as revealed despite AOA (amoA) increasing in number, AOB (amoA) contributing more to ammonia oxidation while nirS proved opportunistic under stress conditions. This was supported by distinct significant correlations among factors, in which soil urease enzymatic activity (S-UE) was associated with AOA (amoA) and nirK, while AOB (amoA) and nirS positively correlated with NH4+ content and soil potential of hydrogen (pH), respectively. Among the applied treatments, high-level NO3− increased total nitrogen content and had a significant effect on soil N-acetyl-β-d-glucosaminidase (S-NAG) and soil acid protease (S-ACPT) activity. In summary, we observed an increase in Larix olgensis growth with high nitrogen retention.