Persistent Challenge Research Articles

Self-Assembly of Nanovesicles for Enhanced Adsorption and Efficient Photodegradation of 2,4,6-Trichlorophenol.

The compound 2,4,6-trichlorophenol poses significant risks to both the aquatic environment and human health. Its inherent persistence and stability present challenges in achieving complete purification, thus warranting its inclusion as a priority pollutant. The present study reports the development of an amphiphilic small-molecule compound that self-assembles into nanovesicles exhibiting remarkable adsorption and photodegradation capabilities. Through the synergistic effects of hydrogen bonding, van der Waals forces, π-π interactions, and electrostatic interactions, these vesicles efficiently adsorb 2,4,6-trichlorophenol from aqueous solutions within 1 min while demonstrating exceptional environmental stability and broad applicability. Upon self-assembly into vesicles, not only are more adsorption sites exposed, but charge separation and migration within the vesicles are also facilitated. Through the synergistic effects of adsorption and photodegradation, complete removal of 2,4,6-trichlorophenol in aqueous solution can be achieved within 8 h while exhibiting excellent recycling capability. This approach offers a viable strategy for designing and synthesizing pure organic photodegradable materials.

A novel BN aromatic module modified near-infrared fluorescent probe for monitoring carbon monoxide-releasing molecule CORM-3 in living cells and animals

Carbon monoxide (CO), a significant gas transmitter, plays a vital role in the intricate functioning of living systems and is intimately linked to a variety of physiological and pathological processes. To comprehensively investigate CO within biological system, researchers have widely adopted CORM-3, a compound capable of releasing CO, which serves as a surrogate for CO. It aids in elucidating the physiological and pathological effects of CO within living organisms and can be employed as a therapeutic drug molecule. Therefore, the pivotal role of CORM-3 necessitates the development of effective probes that can facilitate the visualization and tracking of CORM-3 in living systems. However, creating fluorescent probes for real-time imaging of CORM-3 in living species has proven to be a persisting challenge that arises from factors such as background interference, light scattering and photoactivation. Herein, the BNDN fluorescent probe, a brand-new near-infrared is proposed. Remarkably, the BNDN probe offers several noteworthy advantages, including a substantial Stokes shift (201 nm), heightened sensitivity, exceptional selectivity, and an exceedingly low CORM-3 detection limit (0.7 ppb). Furthermore, the underlying sensing mechanism has been meticulously examined, revealing a process that revives the fluorophore by reducing the complex Cu2+ to Cu+. This distinctive NIR fluorescence “turn-on” character, coupled with its larger Stokes shift, holds great promise for achieving high resolution imaging. Most impressively, this innovative probe has demonstrated its efficacy in detecting exogenous CORM-3 in living animal. It is important to underscore that these endeavors mark a rare instance of a near-infrared probes successfully detecting exogenous CORM-3 in vivo. These exceptional outcomes highlighted the potential of BNDN as a highly promising new tool for in vivo detection of CORM-3. Considering the impressive imaging capabilities demonstrated by BNDN presented in this study, we anticipate that this tool may offer a compelling avenue for shedding light on the roles of CO in future research endeavors.

Reducing Mode Collapse With Monge-Kantorovich Optimal Transport for Generative Adversarial Networks.

Mode collapse has been a persisting challenge in generative adversarial networks (GANs), and it directly affects the applications of GAN in many domains. Existing works that attempt to solve this problem have some serious limitations: models using optimal transport (OT) strategies (e.g., Wasserstein distance) lead to vanishing or exploding gradients; increasing the number of generators can cause several generators focusing on the same mode; and approaches that modify the loss also do not satisfactorily resolve mode collapse. In this article, we reduce mode collapse by formulating it as a Monge problem of OT map. We show that the Monge problem can be transformed to the distribution transformation problem in GAN, and a rectified affine neural network can be considered as a measurable function. In this way, we propose Monge GAN that uses this measurable function to transform the generated data distribution into the original data distribution. We utilize the Kantorovich formulation to obtain the OT cost, which is regarded as the OT distance between the two distributions. Finally, we conduct extensive experiments on both image and numerical datasets to validate our Monge GAN in reducing model collapse.

Beyond Pell restoration: Addressing persistent funding challenges in prison higher education toward racial and economic justice.

Beyond Pell restoration: Addressing persistent funding challenges in prison higher education toward racial and economic justice.

Examining the implementation of the language in education policy in Tana River County: a case of a Pokomo speakers’ school

ABSTRACT The language in education policy (LiEP) in Kenya calls for the use of mother tongue for pre-primary and lower grades of primary education. However, its implementation has been erratic. This study sought to explore the implementation of LiEP in Tsana Primary School, situated in Tana River County, Kenya, where the local language is Kipfokomu. Using an ethnographic approach, the study involved interviews and observations with Grade 3 students, their parents or guardian, and teachers from Grades 1 to 3. Relevant documents were also reviewed, and thematic analysis was conducted on the collected data. The study revealed two main themes: (1) the lack of a supportive environment for Kipfokomu in the classroom, and (2) the presence of Kipfokomu resource materials on the school premises, but their limited availability in the classroom setting. These findings suggest challenges in implementing LiEP, as teachers often prioritise English over the local language. Despite the presence of essential resources intended to support LiEP, the persistent challenges undermine its effectiveness. Understanding these challenges can inform policy improvements and more effective strategies for implementing mother tongue-based education, ensuring that the educational system better serves linguistic and cultural diversity.

Obturator hernia: a persisting clinical diagnostic challenge – a case report

Introduction: The presentation of a strangulated obturator hernia is rare, with it accounting for less than 0.04% of all hernias. Delay in presentation and diagnosis results in complications like bowel ischemia, necrosis, perforation, and peritonitis, thereby increasing morbidity and mortality. Case presentation: The authors report the case of an 85-year-old multiparous woman who presented with a 3-day history of abdominal pain and vomiting. Upon examination, she exhibited hypotension, altered sensorium, and a distended abdomen with visible peristalsis. An abdominal pelvic computed tomography scan confirmed the diagnosis of ‘intestinal obstruction secondary to an incarcerated obturator hernia’. Subsequently, a lower midline laparotomy was performed, successfully reducing the bowel and repairing the hernial orifice. The patient was discharged on the fourth postoperative day, and there has been no hernia recurrence as of her 3-month follow-up. Discussion: The presentation of a strangulated obturator hernia can be elusive. During clinical examination, both the Howship–Romberg sign and the Hannington-Kiffs sign tests may be negative. Laparoscopic obturator hernia repair has been shown to reduce hospital stay and morbidity. A midline laparotomy has the advantage of easy manual reduction, minimizing bowel trauma, accurately accessing the bowel, and facilitating bowel resection. Conclusion: Obturator hernias constitute rare subtypes of abdominal hernias. They typically occur in older women, and patients often present with poor functional status and multiple comorbidities. The clinical diagnostic tests are uncertain, even in patients with a high index of suspicion. Timely diagnosis and appropriate surgical management are crucial for a favorable outcome.

An Integrative Approach to STEM Workforce Preparation in a Biomedical Science Course [Quality Improvement

Successful retention of STEM majors that then successfully transition into the U.S. STEM workforce is a major challenge for institutions of higher education. The present work represents a quality improvement study of a custom-designed professional development course for undergraduate students characterized by an innovative integration of workforce preparation activities into a STEM content knowledge course. The QI study investigated the student-perceived impact of participation in goal setting and skill assessment utilizing an individual development plan and whether participation in the custom-designed training course increased student proficiency in STEM workforce-relevant skills. Program evaluation data was used to address the professional development needs of undergraduate STEM students, who are members of underrepresented groups in biomedical science. Evaluation data was used to design the professional development course to target and customize structures to support student success planning and goal attainment. Analysis of reported outcomes indicates that most students benefited from participation in the course and experienced a positive impact on their proficiency in workforce relevant skills and goal setting. This integrated approach to workforce preparation in STEM may address some of the persistence and retention challenges that threaten the size and diversity of the future STEM workforce within the U.S.

Boosting Cathode Activity and Anode Stability of Lithium-Sulfur Batteries with Vigorous Iodic Species Triggered by Nitrate.

Lithium-sulfur (Li-S) battery with a sulfurized polyacrylonitrile cathode is a promising alternative to Li-ion systems. However, the sluggish charge transfer of cathode and accumulation of inactive Li on anode remain persistent challenges. An advanced electrolyte additive with function towards both cathode and anode holds great promise to address these issues. Herein, we present a new strategy to boost sulfur activity and rejuvenate dead Li simultaneously. In the polar electrolyte containing I2-LiNO3 additives, I3 -/IO3 - are triggered significantly by the reaction between NO3 - and I- ions. The I3 -/IO3 - are reactive to insulated Li2S product of cathode and inactive Li on anode, thus accelerating the conversion reaction of sulfur and recovering Li sources back to battery cycling. The in situ/ex situ spectroscopic and morphologic monitoring reveal the crucial role of iodine in promoting Li2S dissociation and inhibiting dendritic Li growth. With the modified electrolyte, the symmetric Li||Li cells deliver a lifespan of 4000 h with an overpotential less than 12 mV at 0.5 mA cm-2. For Li-S cells, 100 % capacity retention up to thousands of cycles and enhanced rate capability are available. This work demonstrates a feasible strategy on electrolyte engineering for practical applications of Li-S batteries.

Boosting Cathode Activity and Anode Stability of Lithium–Sulfur Batteries with Vigorous Iodic Species Triggered by Nitrate

AbstractLithium–sulfur (Li−S) battery with a sulfurized polyacrylonitrile cathode is a promising alternative to Li‐ion systems. However, the sluggish charge transfer of cathode and accumulation of inactive Li on anode remain persistent challenges. An advanced electrolyte additive with function towards both cathode and anode holds great promise to address these issues. Herein, we present a new strategy to boost sulfur activity and rejuvenate dead Li simultaneously. In the polar electrolyte containing I2−LiNO3 additives, I3−/IO3− are triggered significantly by the reaction between NO3− and I− ions. The I3−/IO3− are reactive to insulated Li2S product of cathode and inactive Li on anode, thus accelerating the conversion reaction of sulfur and recovering Li sources back to battery cycling. The in situ/ex situ spectroscopic and morphologic monitoring reveal the crucial role of iodine in promoting Li2S dissociation and inhibiting dendritic Li growth. With the modified electrolyte, the symmetric Li||Li cells deliver a lifespan of 4000 h with an overpotential less than 12 mV at 0.5 mA cm−2. For Li−S cells, 100 % capacity retention up to thousands of cycles and enhanced rate capability are available. This work demonstrates a feasible strategy on electrolyte engineering for practical applications of Li−S batteries.

Efficient Motif Discovery in Protein Sequences Using a Branch and Bound Algorithm.

Identifying motifs within sets of protein sequences constitutes a pivotal challenge in proteomics, imparting insights into protein evolution, function prediction, and structural attributes. Motifs hold the potential to unveil crucial protein aspects like transcription factor binding sites and protein-protein interaction regions. However, prevailing techniques for identifying motif sequences in extensive protein collections often entail significant time investments. Furthermore, ensuring the accuracy of obtained results remains a persistent motif discovery challenge. This paper introduces an innovative approach-a branch and bound algorithm-for exact motif identification across diverse lengths. This algorithm exhibits superior performance in terms of reduced runtime and enhanced result accuracy, as compared to existing methods. To achieve this objective, the study constructs a comprehensive tree structure encompassing potential motif evolution pathways. Subsequently, the tree is pruned based on motif length and targeted similarity thresholds. The proposed algorithm efficiently identifies all potential motif subsequences, characterized by maximal similarity, within expansive protein sequence datasets. Experimental findings affirm the algorithm's efficacy, highlighting its superior performance in terms of runtime, motif count, and accuracy, in comparison to prevalent practical techniques.

460 Frame Navigation Guided Percutaneous Balloon Compression for Intractable Trigeminal Neuralgia Secondary to Multiple Sclerosis

INTRODUCTION: Patients with multiple sclerosis (MS) are more likely to develop trigeminal neuralgia (TN) compared to the regular population, due to scarring of the nerve and the development of a demyelination plaque. Despite treatment, approximately 10% of MS patients treated for TN experience symptom recurrence, including the development of MS-like symptoms such as optic neuritis and bilateral facial pain. METHODS: A computed tomography (CT) scan was performed preoperatively on two patients diagnosed with multiple sclerosis (MS) who experienced secondary trigeminal neuralgia (TN). The acquired images were then introduced into a navigation system for intraoperative navigation. The preliminary CT images were registered using the navigation system and the bony landmarks were set. Fluoroscopy-guided cannulation was performed under general anesthesia to complete the procedure. RESULTS: Two patients diagnosed with multiple sclerosis (MS) who experienced refractory trigeminal neuralgia (TN) underwent percutaneous balloon compression. The patients were 38 and 56 years old and complained of facial pain in the V1-V2 and V3 distribution. Initial conservative treatment and one dosage of Gamma Knife Radiosurgery (GKR) resulted in symptom control for a few weeks. However, both patients had an acute recurrence of pain; thus, percutaneous retrogasserian balloon compression was performed. During follow-up, the patients reported a 70% decrease in pain after the procedure, with minimal recurrence of shooting episodes. CONCLUSIONS: The management of trigeminal neuralgia secondary to drug-resistant multiple sclerosis presents a persistent challenge. The percutaneous technique for retrogasserian balloon compression may offer a solution for some patients, but it can be technically challenging. Microvascular decompression remains a debated approach for this population, given the complexity of the pathogenesis, target identification, and the potential absence of neurovascular conflict, while stereotactic radiosurgery may be a promising alternative.

Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL (DSIL) emerges as a significant subset of IL methodologies, offering the ability to learn trajectories via movement primitives and policy learning based on experiential abstraction. This paper emphasizes the fusion of theoretical paradigms, integrating control theory principles inherent in dynamical systems into IL. This integration notably enhances robustness, adaptability, and convergence in the face of novel scenarios. This survey aims to present a comprehensive overview of DSIL methods, spanning from classical approaches to recent advanced approaches. We categorize DSIL into autonomous dynamical systems and non-autonomous dynamical systems, surveying traditional IL methods with low-dimensional input and advanced deep IL methods with high-dimensional input. Additionally, we present and analyze three main stability methods for IL: Lyapunov stability, contraction theory, and diffeomorphism mapping. Our exploration also extends to popular policy improvement methods for DSIL, encompassing reinforcement learning, deep reinforcement learning, and evolutionary strategies. The primary objective is to expedite readers’ comprehension of dynamical systems’ foundational aspects and capabilities, helping identify practical scenarios and charting potential future directions. By offering insights into the strengths and limitations of dynamical system methods, we aim to foster a deeper understanding among readers. Furthermore, we outline potential extensions and enhancements within the realm of dynamical systems, outlining avenues for further exploration.

Economic impact of peste des petits ruminants on small ruminant production in Senegal: Gender considerations within a system dynamics modelling approach

CONTEXTPeste des petits ruminants (PPR) remains a persistent challenge for small ruminant production in Senegal, leading to economic losses for livestock keepers. Gender dynamics and socio-cultural factors influence production decisions at the household level. Despite its relevance, economic impact models of PPR have not incorporated gender dynamics. OBJECTIVEThis paper seeks to redress this gap by disaggregating the farm-level impacts of PPR based on the gender of the household head and implementers of routine and management activities in small ruminant production at the household level. METHODSTo quantify the gendered economic impacts of PPR, a system dynamics model was developed with four modules: integrated production-epidemiology, economics and profitability, disease control, and marketing. The model was parameterised based on survey data from 747 households in 49 agro-pastoral and pastoral villages in Senegal and augmented by secondary data. The model was validated (structurally and behaviourally) and simulated for 30 years with a weekly timestep. A set of disease scenarios were run for ten different farm household typologies based on the gender of the household head and household members who perform production management activities (like purchasing, selling, gifting, and vaccinating animals) and routine activities (like feeding, cleaning pens, and caring for sick animals). RESULTS AND CONCLUSIONSModel results estimated annual reductions in farm profitability of 51% to 61% with a hypothetical 25% incidence rate of PPR, corresponding to an average of $1051 to $1246 USD lost per household per year. In the two most impacted household typologies, management activities were performed by either men only or women only, and in the three least impacted household typologies, management activities were shared by men and women. The least impacted household typology with the highest gross margins without PPR ($2429 USD per year) and the lowest percentage of loss (51%) was female-headed households with men and women doing management tasks and primarily women or girls doing routine activities. SIGNIFICANCESHouseholds with both women and men engaged in management activities were less impacted by PPR, highlighting the crucial role of women's agency in management decisions taken in small ruminant production at the farm level. The findings further provide the empirical grounding for considering gender dynamics in interventions to control PPR and other livestock diseases in small ruminant production systems.

Improved hydrodynamic properties and enhanced resistance to particle deposition and membrane fouling by millimeter-scale patterned membranes

Membrane fouling is considered a persistent challenge in membrane technology for water and wastewater treatment. Membrane surface patterning is a chemical-free means of controlling membrane fouling. Previous studies mainly focused on nano- and micro-scale patterns, with little attention paid on millimeter-scale patterns on the membrane surface. In this study, the millimeter-scale patterned microfiltration membranes with different pattern heights (PM50, PM55 and PM60) were fabricated, the performances in resisting particle deposition and membrane fouling with synthetic wastewater were examined compared with flat-sheet membrane (FM), and the underlying hydrodynamic mechanism was unveiled using computational fluid dynamics (CFD). CFD simulation results reveal that compared to micro- and nano-scale patterns, millimeter-scale patterns exert a notable increase in maximum velocity, average shear stress and maximum shear stress. All the three millimeter-scale patterned membranes exhibit superior resistance to both particle deposition and membrane fouling than FM, owing to the improvement of hydrodynamic properties instead of enlarged membrane area. PM55 shows the lowest flux decline ratios and least particle/foulant deposition among the three millimeter-scale patterned membranes in both particle deposition and membrane fouling experiments. Pattern height plays a crucial role, as small pattern height cannot generate significant vortices, while high pattern height leads to flow separation. For PM55, the vortices form in the valley regions and locate close to bulk flow, which help transport the deposited particles or foulants back to the bulk flow. The design of pattern configuration is suggested to be tailored based on particle size. Future research can focus on the pilot testing of millimeter-scale patterned membranes, as well as the long-term stability with actual influent.

Corporate Governance and Performance of Listed Firms in Nigerian Exchange Group

The performance of listed firms in Nigeria is a topic of significant concern and interest among stakeholders, including investors, regulators, and policymakers. Despite the considerable growth and development of the Nigerian economy in recent years, some persistent challenges and issues affect the performance of listed firms, hindering their ability to achieve optimal results and contribute effectively to economic prosperity. There is a growing need to explore the relationship between corporate governance mechanisms and firm performance in Nigerian firms.The study adopted an ex-post facto research design, extracting secondary data from the annual reports of 153 companies listed on the Nigerian Exchange Group (NGX) that made up the study's population. Using a purposive sampling approach, 10 firms were chosen across different industries as the sample size. The scope spanned from 2013 to 2021, a period of nine years, and data underwent descriptive and inferential statistical analyses. The empirical investigations found that board size had a positive significant effect on return on assets, while the number of non-executive directors had a negative significant effect on return on assets. The overall results demonstrated that corporate governance had a significant effect on the firm performance. The findings suggest that companies adhering to robust corporate governance standards tend to excel across various performance metrics compared to those with weaker governance practices. This study recommends that Policymakers of Nigerian firms should consider optimizing board size to enhance performance, ensuring a balance between diversity and efficiency.

Breaking barriers to agroforestry: FAO’s global capacity needs assessment

The FAO's Global Agroforestry Capacity Needs Assessment (CNA) revealed persistent challenges which are hindering widespread adoption of agroforestry despite its recognised benefits. Three primary action areas were identified: transforming agroforestry into an economically viable production system; strengthening enabling environments through agroforestry policies and strategies; and improving agroforestry extension for more biodiverse and agroecological systems. Stakeholders - governmental entities, NGOs, land users and community groups, researchers, private-sector entities and investors and donors - demonstrated expertise in agroforestry planning but identified gaps in socioeconomic aspects, and policy analysis and implementation. Challenges included insecure land tenure, limited market access and insufficient financial incentives for farmers. The assessment emphasised the need for collaborative efforts to address these challenges, recommending tailored approaches for the different stakeholder groups. Recommendations included improving data collection on socioeconomic aspects, developing innovative financing mechanisms and promoting peer-to-peer knowledge exchange. Efforts to strengthen policy frameworks, enhance extension services, and integrate local and Indigenous knowledge were also proposed. Concerted action is, therefore, necessary to unlock agroforestry's full potential in addressing global sustainability challenges to promote sustainable land management and food security worldwide.

Gradient Hydrogels Spatially Trapped Optical Cell Profiling for Quantitative Blood Cellular Osmotic Analysis.

The quantitative exploration of cellular osmotic responses and a thorough analysis of osmotic pressure-responsive cellular behaviors are poised to offer novel clinical insights into current research. This underscores a paradigm shift in the long-standing approach of colorimetric measurements triggered by red cell lysis. In this study, we engineered a purpose-driven optofluidic platform to facilitate the goal. Specifically, creating photocurable hydrogel traps surmounts a persistent challenge─optical signal interference from fluid disturbances. This achievement ensures a stable spatial phase of cells and the acquisition of optical signals for accurate osmotic response analysis at the single-cell level. Leveraging a multigradient microfluidic system, we constructed gradient osmotic hydrogel traps and developed an imaging recognition algorithm, empowering comprehensive analysis of cellular behaviors. Notably, this system has successfully and precisely analyzed individual and clustered cellular responses within the osmotic dimension. Prospective clinical testing has further substantiated its feasibility and performance in that it demonstrates an accuracy of 92% in discriminating complete hemolysis values (n = 25) and 100% in identifying initial hemolysis values (n = 25). Foreseeably, this strategy should promise to advance osmotic pressure-related cellular response analysis, benefiting further investigation and diagnosis of related blood diseases, blood quality, drug development, etc.

Availability, price, and affordability of anti-hepatitis B virus drugs: a cross-sectional study in China.

The global prevalence of hepatitis B virus (HBV) has presented a persistent challenge for public health prevention and treatment. However, studies that assess the public's access to anti-HBV drugs are absent. To examine the availability, pricing, and affordability of anti-HBV drugs in Jiangsu province, China and provide recommendations for improvement. An enhanced methodology developed by the World Health Organization (WHO) and Health Action International was applied in a cross-sectional study that included 1026 healthcare facilities distributed in 13 prefectural-level cities in Jiangsu province. Since almost all drugs had an availability of less than 30%, the accessibility of anti-HBV drugs was notably low. Primary healthcare facilities had the lowest availability, reporting 1.4% for Original Brands (OBs) and 1.7% for lowest-priced generics (LPGs). Furthermore, the northern Jiangsu region recorded the lowest availability at 0.7%. LPGs demonstrated higher availability than OBs, with median availability probabilities of 2.6% and 1.4%, respectively. The drugs listed on the WHO Essential Medicines List exhibited higher availability than those on other lists. The median price ratios for OBs, LPGs, and volume-based purchasing drugs were 0.83, 0.50, and 0.27, respectively, less than 1.5 times the international reference price. Despite favorable pricing, affordability rate was 23% for urban residents and 0% for rural residents, which was discouraging. Low availability and affordability of anti-HBV drugs were observed. Policy recommendations should emphasize the improvement of LPG availability by incentivizing priority prescribing. Healthcare subsidies should be provided more effectively and equitably.

Automated Food Recognition for Nutritional Analysis in Dietary Evaluation

In today's world, computer vision has made remarkable strides, especially in the realm of recognizing food images. Deep neural networks (DNN) have become a standout choice among various machine learning algorithms for their effectiveness in precisely identifying different food items captured in images. Despite the widespread adoption of DNN-based classification algorithms for food recognition, there are persistent challenges in accurately pinpointing foods due to variations in size, shape, and other defining characteristics. This paper aims to provide a brief overview of how deep learning (DL) is leveraged in food recognition and explores its wide-ranging applications. Additionally, it delves into the utilization of machine learning (ML) to construct a robust model for identifying food in images, using the Fruits and Vegetables Image Recognition Dataset. The results highlight a significant improvement in the accuracy of the proposed model, showcasing an impressive increase of around 95%.

Causality and causal inference for engineers: Beyond correlation, regression, prediction and artificial intelligence

AbstractIn order to engineer new materials, structures, systems, and processes that address persistent challenges, engineers seek to tie causes to effects and understand the effects of causes. Such a pursuit requires a causal investigation to uncover the underlying structure of the data generating process (DGP) governing phenomena. A causal approach derives causal models that engineers can adopt to infer the effects of interventions (and explore possible counterfactuals). Yet, and for the most part, we continue to design experiments in the hope of empirically observing engineered intervention(s). Such experiments are idealized, complex, and costly and hence are narrow in scope. On the contrary, a causal investigation will allow us to peek into the how and why of a DGP and provide us with the essential means to articulate a causal model that accurately describes the phenomenon on hand and better predicts the outcome of possible interventions. Adopting a causal approach in engineering is perhaps more warranted than ever—especially with the rise of big data and the adoption of artificial intelligence (AI); wherein AI models are naivety presumed to describe causal ties. To bridge such knowledge gap, this primer presents fundamental principles behind causal discovery, causal inference, and counterfactuals from an engineering perspective and contrasts that to those pertaining to correlation, regression, and AI.This article is categorized under: Application Areas > Industry Specific Applications Algorithmic Development > Causality Discovery Application Areas > Science and Technology Technologies > Machine Learning