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Efficacy and safety of acupuncture treatment for post-stroke depression: A protocol for systematic review and meta-analysis.

Post-stroke depression is a common complication of stroke, with a high incidence rate and low recognition rate. Many patients do not receive effective intervention at the onset, which affects subsequent treatment outcomes. Post-stroke depression not only impacts the patient's mental well-being but also increases the risk of stroke recurrence and poor prognosis. Therefore, it has become a significant public health concern. Acupuncture has gained significant popularity in the treatment of post-stroke depression. However, there are inconsistent clinical research results regarding its efficacy and safety. This systematic review aims to gather and critically assess all available evidence regarding the effectiveness and safety of acupuncture in the treatment of post-stroke depression in patients. We will conduct thorough searches for relevant studies in multiple electronic databases (PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP Database, Wan-fang Data and China Biomedical Database). Our search scope will encompass studies published from the inception of each database until September 2023. To evaluate the potential bias in all the included studies, we will adhere to the guidelines offered in the Cochrane Handbook. The total effective rate will be the primary outcome. To conduct a systematic review, we will employ RevMan 5.4 software. This study will obtain efficacy and safety of acupuncture for the treatment of post-stroke depression. The conclusions of this study will provide evidence-based perspectives that can guide clinical decision-making regarding the practicality and recommended timing of using acupuncture to treat post-stroke depression. Furthermore, this study will help advance the clinical application of acupuncture treatment for post-stroke depression and enhance its efficacy while ensuring patient safety.

Incidence and prognosis of frontal intermittent rhythmic delta activity in King Fahad Hospital-Hofuf intensive care unit

Objective: This study aimed to verify the rate of occurrence of frontal intermittent rhythmic delta activity (FIRDA) in electroencephalograms (EEGs) of adult patients in the intensive care unit (ICU) and its correlation with either neurological or non-neurological etiologies or prognoses. Methods: A retrospective review was performed on 239 EEGs with analysis of their neuroimages and clinical data from an ICU at King Fahad Hospital, Hufof from November 2021 to October 2022. A literature review was also conducted using the PUBMED database. The search scope was covered by the keyword "FIRDA" and was limited to English-language articles. Results: A total of 239 EEGs were conducted in the ICU, 19 (7.9%) had either electrographic or clinical seizures with or without slowing and/or interictal epileptiform discharges, 6 (2.5%) EEGs had FIRDA, and the rest of the EEGs (80.5%) had only generalized slow or rarely other EEG patterns which were noted in coma. Toxic metabolic disturbances and sepsis were the acute diagnoses of all the observed cases. There were no clinical seizures in the patients who had FIRDA and no history of epilepsy. There was no significant association between FIRDA and the seizure occurrence where the p-value was 1 (Confidence interval—0, 10.28). Conclusion: This study provides valuable insights into the incidence, clinical characteristics, and outcomes of FIRDA in critically ill patients admitted to the ICU. While FIRDA remains a relatively uncommon finding, its association with toxic-metabolic disturbances and sepsis underscores the importance of comprehensive neurological monitoring in critically ill patients.

Engineering considerations for practical lithium-air electrolytes.

Lithium-air batteries promise exceptional energy density while avoiding the use of transition metals in their cathodes, however, their practical adoption is currently held back by their short lifetimes. These short lifetimes are largely caused by electrolyte breakdown, but despite extensive searching, an electrolyte resistant to breakdown has yet to be found. This paper considers the requirements placed on an electrolyte for it to be considered usable in a practical cell. We go on to examine ways, through judicious cell design, of relaxing these requirements to allow for a broader range of compounds to be considered. We conclude by suggesting types of molecules that could be explored for future cells. With this work, we aim to broaden the scope of future searches for electrolytes and inform new cell design.

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Reducing Search Regions for Fast Detection of Exact Point-to-Point Geodesic Paths on Meshes.

Fast detection of exact point-to-point geodesic paths on meshes is still challenging with existing methods. For this, we present a method to reduce the region to be investigated on the mesh for efficiency. It is by our observation that a mesh and its simplified one are very alike so that the geodesic path between two defined points on the mesh and the geodesic path between their corresponding two points on the simplified mesh are very near to each other in the 3D Euclidean space. Thus, with the geodesic path on the simplified mesh, we can generate a region on the original mesh that contains the geodesic path on the mesh, called the search region, by which existing methods can reduce the search scope in detecting geodesic paths, and so obtaining acceleration. We demonstrate the rationale behind our proposed method. Experimental results show that we can promote existing methods well, e.g., the global exact method VTP (vertex-oriented triangle propagation) can be sped up by even over 200 times when handling large meshes. Our search region can also speed up path initialization using the Dijkstra algorithm to promote local methods, e.g., obtaining an acceleration of at least two times in our tests.

Psychotic decompensation in the course of SARS-COV-2 infection - case report

Introduction: SARS-CoV-2 is a single-stranded RNA virus belonging to the group of coronaviruses. Among patients infected with the COVID virus, neuropsychiatric symptoms are observed, such as dizziness and headaches, anxiety, depression, as well as delusions and hallucinations. Psychotic symptoms have been observed in patients infected with SARS-CoV-2 without a prior psychiatric history. Aim: The purpose of this study was to present a case of a patient with neuropsychiatric symptoms, most likely caused by a past COVID infection, and to present possible pathophysiological mechanisms explaining neuropsychiatric symptoms in the course of SARS-CoV-2 infection. Material and methods: Literature review was carried out using electronic databases: PubMed, Google Scholar, using the following keywords: SARS-COV-2, neuropsychiatric symptoms, reactive psychosis, cytokine storm, limiting the search scope to 2019-2023. Publications in English were used. The patient's medical records, results of laboratory and imaging tests and descriptions of psychological tests were used to describe the case. Case description: A 27-year-old patient, not previously treated psychiatrically, with a recent COVID-19 infection, who developed acute, quickly passing psychotic symptoms in the form of persecutory delusions and visual and auditory hallucinations in a short period of time. Discussion: The effect of the SARS-CoV-2 virus on the central nervous system is not fully understood - the literature presents several possible mechanisms underlying the neuropsychiatric manifestations of SARS-CoV-19 infection, which include viral infiltration into the central nervous system, dysregulation of the cytokine network and excitotoxicity and translocation of intestinal microbes. Keywords: SARS-COV-2, neuropsychiatric symptoms, reactive psychosis, cytokine storm

Neural Attributed Community Search at Billion Scale

Community search has been extensively studied in the past decades. In recent years, there is a growing interest in attributed community search that aims to identify a community based on both the query nodes and query attributes. A set of techniques have been investigated. Though the recent methods based on advanced learning models such as graph neural networks (GNNs) can achieve state-of-the-art performance in terms of accuracy, we notice that 1) they suffer from severe efficiency issues; 2) they directly model community search as a node classification problem and thus cannot make good use of interdependence among different entities in the graph. Motivated by these, in this paper, we propose a new neur AL attr I buted Community s E arch model for large-scale graphs, termed ALICE. ALICE first extracts a candidate subgraph to reduce the search scope and subsequently predicts the community by the Consistency-aware Net, termed ConNet. Specifically, in the extraction phase, we introduce the density sketch modularity that uses a unified form to combine the strengths of two existing powerful modularities, i.e., classical modularity and density modularity. Based on the new modularity metric, we first adaptively obtain the candidate subgraph, formed by the k-hop neighbors of the query nodes, with the maximum modularity. Then, we construct a node-attribute bipartite graph to take attributes into consideration. After that, ConNet adopts a cross-attention encoder to encode the interaction between the query and the graph. The training of the model is guided by the structure-attribute consistency and the local consistency to achieve better performance. Extensive experiments over 11 real-world datasets including one billion-scale graph demonstrate the superiority of ALICE in terms of accuracy, efficiency, and scalability. Notably, ALICE can improve the F1-score by 10.18% on average and is more efficient on large datasets in comparison to the state-of-the-art. ALICE can finish training on the billion-scale graph within a reasonable time whereas state-of-the-art can not.

Exploring mixed lepton-quark interactions in non-resonant leptoquark production at the LHC

Searches for new physics (NP) at particle colliders typically involve multivariate analysis of kinematic distributions of final state particles produced in a decay of a hypothetical NP resonance. Since the pair-production cross-sections mediated by such resonances are strongly suppressed by the NP scale, this analysis becomes less relevant for NP searches for masses of the BSM resonance above 1 TeV. On the other hand, t-channel processes are less sensitive to the mass of the virtual mediator and therefore larger phase-space can be potentially probed as well as the couplings between the NP particles and the Standard Model fields. The fact that transitions between different generations of quarks and leptons may exist, the potential of the search presented in this article can be used, as a reference guide, to enlarge significantly the scope of searches performed at the LHC to flavour off-diagonal channels, in a theoretically consistent approach. In this work, we study non-resonant production of scalar leptoquarks which have been proposed in the literature to provide a potential avenue for radiative generation of neutrino masses, accommodating as well the existing flavour physics data. Final states involving just two muons at the LHC (μ+μ−), are used as a well-motivated case study.

How to Improve School Leadership (in Beijing's High Schools)

Educational leadership plays a crucial role in the academic performance and direction of students' education. Currently, schools in Beijing are prioritizing the impact of educational leadership. This paper contributes to the author's scholarly research, synthesizing various factors that affect educational leadership in Beijing schools. The paper is structured into four parts. Firstly, it presents the background and current developments regarding the research topic. Secondly, the methodology section outlines the scope of literature search and the search method employed in this paper. The main part of the literature review provides a critical reflection and narrative analysis of the influencing factors on educational leadership in Beijing schools. The paper analyses five key topics related to educational leadership in Beijing's high school education system. These topics are Changes in the roles and responsibilities of educational leaders, Collaboration and Partnership in Educational Leadership, Distributed Leadership, Instructional and Transformational Leadership, Practical Training for school leaders, finally draws conclusions so that future research can further explore how to enhance and improve educational leadership in Beijing's high school education system.

Colonoscopy: body and psyche. Can psychiatry contribute to the quality of the examination?

Introduction: The relationship between a state of a body and mind is well known, although difficult to grasp. Its existence is reflected in subsequent editions of the classifications of mental disorders. One important and frequently performed medical procedure that significantly interferes with the patient's somatic state is colonoscopy. The aim of this study was to review the literature on the patient's psychological functioning in the context of colonoscopy. Material and methods: A review of literature in PubMed and Scopus databases was conducted. Keywords used were: colonoscopy, psychiatry, mental disorders, psychogastroenterology, gut-brain axis, anxiety, depression, cognitive functions, pain perception, limiting the search scope to 2013-2023. Discussion: The bidirectional relationship between psychological factors and a range of functional gastrointestinal disorders, contributed to the introduction of the term psychogastroenterology. One of its important areas is the research on the gut-brain axis. The interplay between the gut microbiome and mental functioning is apparent. Colonoscopy disrupts the composition of the intestinal microflora. However, despite its invasiveness, it is still often irreplaceable for the diagnosis and treatment of bowel diseases (including colorectal cancer). Higher adherence is needed, which could be achieved by improving patient comfort. An increased level of anxiety before the procedure and its negative impact on cognitive functioning is observed. Negative affect amplifies the experience of pain. Colonoscopy technique continues to be developed. A comprehensive description of the patient's psychological functioning in a colonoscopy situation is still lacking. Conclusions: A comprehensive description of the patient's affective and cognitive determinants in the context of colonoscopy and the associated pain and discomfort would be advisable. Keywords: psychiatry, gastroenterology, colonoscopy, gut-brain axis

Smart city healthcare delivery innovations: a systematic review of essential technologies and indicators for developing nations

BackgroundIn recent times, the concept of smart cities has gained remarkable traction globally, driven by the increasing interest in employing technology to address various urban challenges, particularly in the healthcare domain. Smart cities are proving to be transformative, utilizing an extensive array of technological tools and processes to improve healthcare accessibility, optimize patient outcomes, reduce costs, and enhance overall efficiency.MethodsThis article delves into the profound impact of smart cities on the healthcare landscape and discusses its potential implications for the future of healthcare delivery. Moreover, the study explores the necessary infrastructure required for developing countries to establish smart cities capable of providing intelligent health and care services. To ensure a comprehensive analysis, we employed a well-structured search strategy across esteemed databases, including PubMed, OVID, EMBASE, Web of Science, and Scopus. The search scope encompassed articles published up to November 2022, resulting in a meticulous review of 22 relevant articles.ResultsOur findings provide compelling evidence of the pivotal role that smart city technology plays in elevating healthcare delivery, forging a path towards improved accessibility, efficiency, and quality of care for communities worldwide. By harnessing the power of data analytics, Internet of Things (IoT) sensors, and mobile applications, smart cities are driving real-time health monitoring, early disease detection, and personalized treatment approaches.ConclusionSmart cities possess the transformative potential to reshape healthcare practices, providing developing nations with invaluable opportunities to establish intelligent and adaptable healthcare systems customized to their distinct requirements and limitations. Moreover, the implementation of smart healthcare systems in developing nations can lead to enhanced healthcare accessibility and affordability, as the integration of technology can optimize resource allocation and improve the overall efficiency of healthcare services. It also may help alleviate the burden on overburdened healthcare facilities by streamlining patient care processes and reducing wait times, ensuring that medical attention reaches those in need more swiftly.

Predicting astrometric microlensing events from Gaia Data Release 3

ABSTRACT Currently, astrometric microlensing is the only tool that can directly measure the mass of a single star; it can also help us to detect compact objects such as isolated neutron stars and black holes. The number of microlensing events that are being predicted and reported is increasing. In this paper, potential lens stars are selected from three types of stars: high-proper-motion stars, nearby stars, and high-mass stars. For each potential lens star, we select a larger search scope to find possible matching sources and to avoid missing events as much as possible. Using data from Gaia Data Release 3, we predict 4500 astrometric microlensing events with δθ+ > 0.1 mas, which occur between J2010.0 and J2070.0, where 1664 events are different from those found previously. There are 293 lens stars that can cause two or more events, where five lens stars can cause more than 50 events. We find that 116 events have the distance of background stars from the proper motion path of lens stars more than 8 arcsec in the reference epoch, where the maximum distance is 16${_{.}^{\prime\prime}}$6. Thus, the cone search method of expanding the search range of sources for each potential lens star can reduce the possibility of missing events.

MOCPSO: A multi-objective cooperative particle swarm optimization algorithm with dual search strategies

Particle swarm optimization (PSO) is a widely embraced meta-heuristic approach to tackling the complexities of multi-objective optimization problems (MOPs), renowned for its simplicity and swift convergence. However, when faced with large-scale multi-objective optimization problems (LSMOPs), most PSOs suffer from limited local search capabilities and insufficient randomness. This can result in suboptimal results, particularly in high-dimensional spaces. To address these issues, this paper introduces MOCPSO, a Multi-Objective Cooperative Particle Swarm Optimization Algorithm with Dual Search Strategies. MOCPSO incorporates a diversity search strategy (DSS) to augment perturbation and enhance the local search scope of particles, alongside a more convergent search strategy (CSS) to expedite particle convergence. Moreover, MOCPSO utilizes a three-category framework to effectively leverage the benefits of both DSS and CSS. Experimental results on benchmark LSMOPs with 500, 1000, and 2000 decision variables demonstrate that MOCPSO outperforms existing state-of-the-art large-scale multi-objective evolutionary algorithms on most test instances.

Technological trends in wheeled armoured vehicle from patent analysis

This study analyzed the domestic and foreign patent applications and technology trends of wheeled armored vehicles. Patents for wheeled armored vehicles have been steadily filed since 2009. As the technology development of armoured vehicles has achieved maturity, this technology has entered stabilization period. The scope of patent technology search is to select patent documents from the five major patent application countries that have applied for a large number of patents globally in both quantity and quality, and the search focused on patents published and registered between 1990 and August 2019 using the WIPS ON program. In particular, the main technologies of wheeled armored vehicles, such as protection, mobility, firepower, and body technology, were analyzed. Technological trend data from patents can be used to create requirements for weapons systems. If technology trends are not sufficiently reflected when raising requirements for a weapon system, problems such as obsolete technology development and lack of operational effectiveness may occur, if an excessive required operational capability (ROC) is set without considering the technology, there is a risk that the target performance may not be reached, costs may be incurred and additional time may be needed. So, for the development of weapon systems, it is necessary to analyze the trends of major technologies and find promising technologies in the long term and of wheeled armored vehicles and the latest development trends of major countries are investigated, as well as the technical information necessary for the establishment of the maneuvering system, which is the cornerstone of Army TIGER 4.0.

A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes

Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.

A photovoltaic parameter identification method based on Pontogammarus maeoticus swarm optimization

Currently, the improvement of model parameter extraction accuracy is essential to research photovoltaic (PV) fields. In this study, a model parameter identification based on Pontogammarus maeoticus swarm optimization (PMSO) is proposed. The PMSO is used for parameter identification of mathematical models for PV modules. In the PMSO algorithm, by giving the ability of free exploration to particles that are far away from the optimal solution, the search scope is expanded to avoid falling into the local optimum. Besides, the local search for each Gammarus has a better convergence for PV parameter identification. Therefore, the accuracy of parameter identification for modeling PV modules is improved. The feasibility and superiority of the proposed method are verified by measured I-V characteristics of the PV array. The experimental results and error analysis verify that when compared with the conventional meta-heuristic algorithms, the proposed method achieves higher modeling accuracy. The proposed PMSO algorithm is suitable for engineering application of parameter identification and modeling of PV modules.

Research Progress and Trends in the Field of Satellite Ozone from 2005 to 2023: A Bibliometric Review

Ozone, an important atmospheric constituent, affects various processes in the troposphere–stratosphere region and significantly contributes to climate and environmental change. The advancement of meteorological satellite technology has enabled the deployment of ozone detection instruments in space, providing accurate and global satellite ozone data in all weather conditions. This study employs scientometric methods, such as collaboration analysis, co-citation analysis, and keyword co-occurrence analysis to investigate the current status, trends, and future directions of satellite ozone research, with a broader search scope and more objective results compared with a manual review. Analyzing a dataset of 5320 bibliographic records from the WoS core collection database reveals the key intellectual frameworks shaping this field during the period from 2005 to 2023. The findings indicate that leading nations, like the United States, Germany, France, and China, along with their respective institutions and authors, spearhead satellite ozone research. Collaborative partnerships between the United States and European countries play a crucial role in advancing research efforts. Moreover, 20 distinct co-citation clusters identify the knowledge framework within the field, demonstrating a consistent progression over time. The focus has expanded from satellite ozone observation instruments to encompass broader areas, such as atmospheric pollution and environmental conditions, with “air quality” emerging as a prominent research area and future trend. Based on these insights, four major research directions are proposed: understanding atmospheric pollution mechanisms, improving ozone detection technologies, utilizing satellite ozone data for weather, and climate phenomena. This study aims to assist scholars by providing a comprehensive understanding of the developmental trajectory of satellite ozone research. Its results can serve as a valuable reference for researchers to identify relevant publications and journals efficiently. Policymakers can also utilize this systematic review as a structured point of reference.

Potential of plant DNA information in determining the provenance and identify of unknown victims

Determination of the personal identity of victims is particularly important for the settlement of criminal cases. Unfortunately, useful information for identification is not always available. We here propose that the particles (pollens) of some plants with specific geographical distributions extracted from human lung tissues contribute to further determining the provenance or long-term residence of unknown victims, thereby considerably narrowing the search scope of the victims. We collected lung tissues from 155 victims with diverse causes of death, extracted DNA from lung tissues, sequenced the DNA fragments of plants on the Illumina Hiseq platform, and barcoded the plant species using phylogenetic methods. Finally, 108 unique plant sequences were detected in 55 samples and identified to belong to 36 species in 32 genera of 29 families. These plants were predominantly insect-pollinated crops and ornamental plants. No significant difference was observed between male and female samples, between urban and rural samples, or among samples of different ages and different sample sizes. There were 16 samples with 21 wild plant species. The original sources of 15 samples were overlapped with the distribution regions of detected plants; 2 samples narrowed the original sources to 2 provinces, which were quite coincident with their source places; 1 sample had no overlapping with its victim source region. Although plant information was only found in one-third of the samples, we further demonstrated the great potential of plant eDNA in identifying the source of unnamed corpses in a real-world case. We used plant eDNA from lung tissues to explore the provenance of an unknown female corpse found in Beijing. The source place of this victim was narrowed to Guangdong and Guangxi provinces, and finally, we confirmed her true identity in the list of missing persons in Guangxi Province. In the presence of a well-covered local reference library, the plant species detected in the lungs can be accurately identified. In difficult criminal cases where physical evidence is relatively weak, plant DNA information may provide new clues. In conclusion, the plant particles trapped in the lungs are promising to help forensic experts narrow the search scope for the identity of unknown victims.

Artificial intelligence and machine learning in neurosurgery: A review of diagnostic significance and treatment planning efficiency

This review analyzes the significance of artificial intelligence (AI) and deep learning (DL) approaches used in radiology in neurosurgery patients and compares AI applications with human models to determine the applicability of AI in disease diagnosis, decision-making, and outcome prediction. A systematic review was conducted from 1997 to 2020 from the PubMed (MEDLINE) database. The search strategy adhered to guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The keywords used for the literature search included “Deep learning,” “Neurosurgery,” “Artificial Intelligence,” “Brain,” “Magnetic resonance imaging-MRI Brain,” and “Machine learning.” The studies focusing on the significance of DL and comparing AI applications with radiologists or clinical experts to enhance diagnostic protocols were included, whereas non-English articles, animal studies, articles lacking full text, and publications such as commentaries, technical notes, abstracts, editorials, opinions, and letters were excluded. A total of 24 articles were included in the review. The P value was observed in 44 out of 63 outcome measures (70%), out of which in 26 out of 63 outturn measures, artificial application subset machine learning (ML) has a significant edge over clinical diagnosis (P < 0.05). The review highlights the potential impact of AI-driven advancements in clinical radiology on enhancing treatment plans for neurosurgery patients, emphasizing the benefits of early intervention, cost reduction, time-saving approaches, and judicious health-care resource utilization. The study’s limitations include potential constraints in identifying relevant literature due to the selected search scope and inclusion criteria, not including studies published outside the specified timeframe and database, and a small number of included studies. Consequently, there is a risk of overlooking innovative methodologies or ground-breaking studies contributing to a more comprehensive understanding of AI applications in neurosurgery. Furthermore, the exclusion of certain publication types, such as commentaries, and conference papers may limit the diversity of different perspectives. However, the study highlights the potential of ML in neurosurgery and the importance of addressing variability in study design, patient populations, and outcome measures in future research to enhance the applicability of AI-driven approaches in clinical practice. It is imperative to recognize and address these challenges to understand the opportunities and limitations inherent in the integration of AI in neurosurgical practice.

Insights and Evidence on Energy Retrofitting Practices in Rural Areas: Systematic Literature Review (2012–2023)

Rural commons face extraordinary challenges like fragility and sensitivity due to climate change. Retrofitting rural built environments affords benefits that could overcome these challenges and support sustainable development. However, notwithstanding the vast energy retrofitting interventions available, the associated aspects require investigation, particularly in distinct rural contexts with all their valuable, cultural, and historical inheritance. Hence, this study aimed to examine energy retrofitting practices in rural settlements worldwide over a decade to diagnose the goals that are being undertaken, stakeholder engagement, and finally, the bi-correlation between rural contexts and interventions, and retrofitting contributions to valorizing the place’s identity. This study is a systematic literature review (SLR) considering the items of the PRISMA checklist (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). An SLR of published peer-reviewed studies between January 2012 and March 2023 in 16 electronic databases in all available languages, using a combination of seven keywords within three domains, was conducted. The initial search resulted in 397; after applying the inclusion/exclusion criteria, there were 60 eligible articles. The academic progress and tendencies in the energy retrofitting domain of rural built environments are discussed and summarized into four major thematic classifications (energy efficiency strategies, energy efficiency planning, policy evaluation, and occupant behavior). Briefly, rural buildings lack energy-saving designs. Simulation tools are essential; however, they should be calibrated with on-site conditions, showing the reasons for selecting the applied retrofitting measures and correlation with the surrounding context. Successful implementation requires cross-disciplinary collaboration, engaging decision makers, and providing energy education for the local community. Regulations should include micro-context-specific environmental performance indicators. These insights could help map out future academic pursuits and help the stakeholders better understand their nature. Simultaneously, this study assists early-stage researchers in conducting systematic literature reviews utilizing different tools. However, the SLR protocol may have limited findings due to the specific search terms used, so the authors believe the more the literature search scope is broadened, the more discoveries could be made.