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ObjectivesUnintended pregnancy is an individual and public health problem with significant social and economic consequences. The literature has established that parents, especially mothers, play an important role in shaping the contraceptive attitudes and behaviors of young women and could therefore affect the likelihood of their daughter experiencing an unintended pregnancy. However, research has yet to fully explore the nuances of how mothers influence their daughters with respect to contraception.MethodsWe conducted a mixed methods study to explore the impact of mothers on women’s contraceptive attitudes and behaviors. In-depth interviews were conducted with 86 women of reproductive age to identify potential patterns and explore the nature of mothers’ influences. We then analyzed medical and prescription claims for a cohort of 9813 pairs of women (mother–daughter proxies) enrolled in Medicaid, to determine if such patterns of contraceptive use held in a larger sample.ResultsIn-depth interviews reveal how and why mothers shape women’s contraceptive attitudes and behaviors, particularly highlighting the nuances of communication, knowledge, and relationships. The statistical claims data supported such findings on a broader scale. For instance, across several types of contraceptives, including oral, injectable, and long-acting reversible contraceptives (LARCs), young women were significantly more likely to use a particular method if an older woman in the household (mother proxy) also used that method (AOR (95% CI) 1.99 (1.67–2.37), 2.06 (1.58–2.68) and 2.83 (1.64–4.88) respectively).Conclusions for PracticeThis study fills a gap in the literature regarding the nuanced ways in which mothers influence women’s contraceptive behavior. In turn, it supports the importance of familial context—especially the influence of mothers—in contraception decision-making and suggests that interventions aimed at improving access to and uptake of effective methods of contraception consider this context in their design and implementation.

The present study was conducted in the surrounding areas of Hadiara drain a major drain flushing out in river Ravi. Various physical and chemical parameters were analyzed in the drinking water randomly collected from five areas located around Hadiara drain including Raja Bolay Village (RBV) Ghanakar Village (GV) Deu Khurd (DK) Kahna Nau (KN) and Green Cap Housing Society (GCHS). The results were compared with standard value of World Health Organization for drinking water. It was observed that the pollutants emanating from local tannery and textile effluents discharged into Hadiara drain increases the value of electrical conductivity and dissolved oxygen in water which is above the WHO standard value. Among the heavy metals tested Cr was higher than the recommended values in all areas studied except in RBV and GCHS. The study concluded that the use of polluted water degraded the ground water quality.

The number of internally displaced persons (IDPs) around the world today is greater than at any point in human history. IDPs represent some the most vulnerable population groups and have specific needs stemming from their displacement experiences. The inability to meet these needs can have implications for their overall wellbeing and impede their efforts to effectively resolve their displacement. Despite the existence of a substantial body of literature on internal displacement, research on approaches to identify the context-specific conditions that exacerbate vulnerability among IDPs is lacking. This dissertation aims to fill this gap in research by demonstrating the utility of a public health approach to the understanding of vulnerability among IDPs in Burundi and Somalia. Poor health is an essential aspect of overall vulnerability and has been attributed to many of the same underlying displacement related conditions that lead to increased vulnerability among IDPs. This dissertation proposes that using public health concepts to identify the social determinants of health among displaced populations can provide key insights into the understanding of conditions that exacerbate vulnerability. The research approach to demonstrate the proposed overlap between health and vulnerability included the examination of two separate data sources. First, secondary data collected from IDP settlements in Burundi and Hargeisa (Somalia) were analyzed using regression models and latent class analyses to identify the conditions that impact IDP’s health status, access to healthcare services and displacement resolution planning as well as to explore the determinants of overall vulnerability. Second, a content analysis of literature produced by key policy actors and stakeholders was conducted to determine the extent to which the conditions that lead to poor health and increased vulnerability among displaced populations are recognized in the displacement narrative relevant to Burundi and Somalia. The research designs of the statistical and content analyses were grounded within Vulnerable Populations Approach (VPA) and existing literature on health and vulnerability among IDPs. The findings of the statistical analysis showed that social determinants of health are key drivers of health status among IDPs in Burundi and Somalia. Furthermore, several indicators of health status also impacted access to healthcare services and influenced IDP’s decision making processes related to displacement resolution. Notably, there was substantial overlap between the conditions that influence the health aspects of displacement and those that lead to increased vulnerability among IDPs. Findings from the content analysis showed that while gaps remain with respect to the systematic examination of conditions that shape health and vulnerability in displacement contexts, over half of the indicators of poor health are recognized to influence overall vulnerability among IDPs. The use of a public health approach in displacement research can facilitate the understanding of displacement related vulnerabilities among IDPs. This can allow humanitarian and policy actors to devise effective interventions aimed at improving IDP’s wellbeing and facilitating the achievement of sustainable displacement resolution.

Efficacy of behavioral interventions to improve maternal health seeking behavior (MHSB) has been increasingly scrutinized, particularly in developing countries like Pakistan. The purpose of this study is to analyze the impact the Pakistan Initiative for Mothers and Newborns (2004-2010) in 22 exposure districts compared to districts with no intervention. The impact of socioeconomic and demographic indicators in comparison to that of the intervention is also discussed in this paper. Data were obtained from the Pakistan Living Standards Measurement Survey 2004-05 and 2012-13.The sample consisted of 54706 women of reproductive age (15-50 years), from 22 intervention districts, and 72 control districts. Five utilization oriented indicators of MHSB were investigated; antenatal visits, tetanus toxoid vaccinations, health facility birth, skilled birth attendance and postnatal care. Using the Difference in Differences technique, binary logistic regression was used to assess the impact of PAIMAN relative to socioeconomic and demographic variables. We find that the PAIMAN intervention had no significant impact on improving likelihood of women seeking maternal health care. With the exception of prenatal care, there were no statistically significant differences in the odds of women utilizing maternal health services in the intervention districts in comparison to the control districts.

Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10-fold cross-validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package.

Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%.

Background and Objective: Vitiligo is a chronic skin damage disease, triggered by differential melanocyte death. Vitiligo (0.5%–1% of the population) is one of the most severe skin conditions. In general, the foundation of the condition of vitiligo remains gradual patchy loss of skin pigmentation, overlying blood, and sometimes mucus. This paper provides a systematic review of the relevant publications and conference papers based on the subject of vitiligo diagnosis and confirmation through computer-aided machine learning (ML) techniques.Materials and Methods: A search was conducted using a predetermined set of keywords across three databases, namely, Science Direct, PubMed, and IEEE Xplore. The selection process involved the application of eligibility criteria, which led to the inclusion of research published in reputable journals and conference proceedings up until June 2024. These selected papers were then subjected to full-text screening for additional analysis. Research publications that involved application of ML techniques with targeted population of vitiligo were selected for further systematic review.Results: Ten selected and screened studies are included in this systematic review after applying eligibility criteria along with inclusion and exclusion criteria applied on initial search result which was 244 studies based on vitiligo. Priority is given to those studies only which use ML techniques to perform detection and diagnosis on vitiligo-targeted population. Data analysis was carried out only from the selected and screened research articles that were published in authentic journals and conference proceedings.Conclusion: The importance of applying ML techniques in the clinical diagnosis of vitiligo can give more accurate results and at the same also eliminate the need of biased human judgement. Based on a comprehensive examination of the research, encompassing the methodologies employed and the metrics utilized to assess outcomes, it was determined that there is a need for further research and investigation regarding the application of ML algorithm for the detection and diagnosis of vitiligo with different datasets and more feature extraction.

Background With rapid industrial development, heavy metal contamination has become a major public health and ecological concern worldwide. Although knowledge about metal pollution in European water resources is increasing, monitoring data and assessments in developing countries are rare. In order to protect human health and aquatic ecosystems, it is necessary to investigate heavy metal content and its consequences to human health and ecology. Accordingly, we collected 200 water samples from different water resources including groundwater, canals, river and drains, and investigated metal contamination and its implications for human and ecological health. This is the first comprehensive study in the region that considered all the water resources for metal contamination and associated human health and ecological risks together. Results Here we show that the water resources of Lahore (Pakistan) are highly contaminated with metals, posing human and ecological health risks. Approximately 26% of the groundwater samples are unsuitable for drinking and carry the risk of cancer. Regarding dermal health risks, groundwater, canal, river, and drain water respectively showed 40%, 74%, 80%, and 90% of samples exceeding the threshold limit of the health risk index (HRI > 1). Regarding ecological risks, almost all the water samples exceeded the chronic and acute threshold limits for algae, fish, and crustaceans. Only 42% of groundwater samples were below the acute threshold limits. In the case of pollution index, 72%, 56%, and 100% of samples collected from canals, river Ravi, and drains were highly contaminated. Conclusions In conclusion, this comprehensive study shows high metal pollution in water resources and elucidates that human health and aquatic ecosystems are at high risk. Therefore, urgent and comprehensive measures are imperative to mitigate the escalating risks to human health and ecosystems.

Voice pathologies are irregular vibrations produced due to vocal folds and various factors malfunctioning. In medical science, novel machine learning algorithms are applied to construct a system to identify disorders that occur invoice. This study aims to extract the features from the audio signals of four chosen diseases from the SVD dataset, such as laryngitis, cyst, non-fluency syndrome, and dysphonia, and then compare the four results of machine learning algorithms, i.e., SVM, Naïve Byes, decision tree and ensemble classifier. In this project, we have used a comparative approach along with the new combination of features to detect voice pathologies which are laryngitis, cyst, non-fluency syndrome, and dysphonia from the SVD dataset. The combination of specific 13 MFCC (mel-frequency cepstral coefficients) features along with pitch, zero crossing rate (ZCR), spectral flux, spectral entropy, spectral centroid, spectral roll-off, and short term energy for more accurate detection of voice pathologies. It is proven that the combination of features extracted gives the best product on the audio, which split into 10 ms. Four machine learning classifiers, SVM, Naïve Bayes, decision tree and ensemble classifier for the inter classifier comparison, give 93.18, 99.45,100 and 51%, respectively. Out of these accuracies, both Naïve Bayes and the decision tree show the most promising results with a higher detection rate. Naïve Bayes and decision tree gives the highest reported outcomes on the selected set of features in the proposed methodology. The SVM has also been concluded to be the commonly used voice condition identification algorithm.

In recent decades, the use of pesticides has become fundamental to agricultural growth. However, the persistent and toxic nature of pesticides has led to significant concerns regarding their ecological and human health consequences. Therefore, for a better understanding of pesticide contamination and its potential risks, here we assessed the levels of five emerging pesticides—acetochlor, imidacloprid, MCPA, atrazine, and allethrin—in soil samples from ponds used for irrigation and in drinking water samples from nearby areas in Lahore, Pakistan. Our findings revealed that 100% of the samples were contaminated, posing substantial ecological and human health risks. Based on the toxic units (TUsum), all the soil samples showed higher toxic pressure, exceeding acute and chronic toxicity thresholds for earthworms, while 100% of water samples posed chronic toxicity risks to crustaceans and 10% to algae. Pollution index (PI) analysis further classified 100% of the soil samples and 10% of the water samples as highly polluted. These findings show high-pesticide residues in both soil and water and highlight immediate risk assessment and mitigation measures to protect non-target organisms. This preliminary information can be used to adopt risk assessment monitoring programmes and help higher authorities in making policies and guidelines to mitigate the escalating risk for ecology and humans.