Relevant Information Research Articles

The role of supply chain management in improving performance of Jordanian small and medium enterprises

Supply chain management practices are always helpful for multinational enterprises in increasing their growth. However, there has always remained an open debate about which practices are helpful for small and medium enterprises (SMEs). Therefore, this study has examined the influence of supply chain management practices on the performance of SMEs. Moreover, it has examined how the moderation of supply chain ecocentricity and innovation capabilities strengthens or weakens the relationship between supply chain management practices and SMEs’ performance. The quantitative study involved 352 SME managers as they can provide the relevant information and possess complete knowledge of management practices. The data were gathered via a questionnaire, and the responses were analyzed using IBM SPSS 28 and IBM AMOS. The results show that SMEs’ performance is linked to supply chain management practices (β = 0.544; P < .001), and this linkage is further strengthened by supply chain ecocentricity (R2 change = 0. 082***, F-Statistics = 47.18) and innovation capabilities (R2 change = 0.061***, F-Statistics = 39.74). SMEs can enhance their performance by efficiently incorporating supply chain management practices and developing innovation capabilities and supply chain ecocentricity. AcknowledgmentsThis study is funded by Al Hussein Bin Talal University, Ma’an, Jordan, No. 125/2023.

Dairy input agri-entrepreneurs’ practices and their effect on inputs market participation by smallholder farmers in Tanzania

Dairy productivity per cow remains lower than the production ability amid increasing population. Among major reasons for this is the lower uptake of improved dairy inputs by smallholder dairy farmers. We characterized dairy input providers' business practices using Latent class analysis (LCA) and determined the effect of these business practices on the uptake of inputs by smallholder dairy farmers in Tanga and Kilimanjaro regions using OLS. From LCA, Bayesian Information Criteria (BIC) revealed a 3-class model as the most parsimonious and best fit (2153.071). The latent class (LC) membership of the input/service entrepreneurs for classes I, II, and III was 37.6% (35), 9.7% (9), and 52.7% (49), respectively. Using item response probabilities, we classified class I, II and III as ‘High business activities and business promotional practices’, ‘Low business activities, medium business promotional activities’, and ‘Medium business activities, Low business promotional activities’. Extension services provision was significant across all the classes with probability values greater than 0.5. The OLS model revealed that LC I and II were significant at 1% while LC III was significant at 5%. Education level and Moshi Rural District were negatively significant. Technical training of input entrepreneurs and capacity building on bundling relevant information alongside inputs is necessary for knowledge sharing with farmers. Networking, collaborations, and alliances among input/service entrepreneurs and between entrepreneurs and dairy farmers should be fostered for adequacy in technical information sharing and connecting farmers to other input suppliers. There is need for entrepreneurs to invest in rural areas and improve information and input access by the farmers. The study explored private dairy entrepreneurs as alternative extension services providers as a business strategy to enhance uptake of improved inputs by smallholder farmers. This study is relevant in making workable decisions towards implementing programmes geared towards increasing the improved input uptake in developing countries.

Nilai-Nilai Pendidikan Karakter pada Novel “Edensor” (Novel Ketiga dari Tetralogi Laskar Pelangi) Karya Andrea Hirata

This study aims to describe the 9 pillars of character education values contained in the novel “Edensor” by Andrea Hirata. A number of steps to analyze this research are (1) reading novels, (2) trying to understand, (3) analyzing novel stories can be known, (4) making discussions, (5) drawing conclusions. The results of this study show that the 9 pillars of character education values contained in the novel “Edensor” (1) amounted to 18 citations at the elementary school, (2) amounted to 9 citations at the junior high school, (3) amounted to 15 citations at the high school, and (4) amounted to 46 citations at the college. This study concluded that after being tested by analyzing the novel “Edensor”, it is hoped that this scientific work will be able to provide accurate and relevant information about the value of character education contained in this novel.

Value of Immediate Flat Panel Perfusion Imaging after Endovascular Therapy (AFTERMATH): A Proof of Concept Study.

Potential utility of flat panel CT perfusion imaging (FPCT-PI) performed immediately after mechanical thrombectomy (MT) is unknown. We aimed to assess whether FPCT-PI obtained directly post-MT could provide additional potentially relevant information on tissue reperfusion status. This was a single-center analysis of all patients with consecutive acute stroke admitted between June 2019 and March 2021 who underwent MT and postinterventional FPCT-PI (n = 26). A core lab blinded to technical details and clinical data performed TICI grading on postinterventional DSA images and qualitatively assessed reperfusion on time-sensitive FPCT-PI maps. According to agreement between DSA and FPCT-PI, all patients were classified into 4 groups: hypoperfusion findings perfectly matched by location (group 1), hypoperfusion findings mismatched by location (group 2), complete reperfusion on DSA with hypoperfusion on FPCT-PI (group 3), and hypoperfusion on DSA with complete reperfusion on FPCT-PI (group 4). Detection of hypoperfusion (present/absent) concurred in 21/26 patients. Of these, reperfusion findings showed perfect agreement on location and size in 16 patients (group 1), while in 5 patients there was a mismatch by location (group 2). Of the remaining 5 patients with disagreement regarding the presence or absence of hypoperfusion, 3 were classified into group 3 and 2 into group 4. FPCT-PI findings could have avoided TICI overestimation in all false-positive operator-rated TICI 3 cases (10/26). FPCT-PI may provide additional clinically relevant information in a considerable proportion of patients undergoing MT. Hence, FPCT-PI may complement the evaluation of reperfusion efficacy and potentially inform decision-making in the angiography suite.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BackgroundPathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30–40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60–70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.MethodsH&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.ResultsThe tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.ConclusionOur machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Pose-based tremor type and level analysis for Parkinson's disease from video.

Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73 and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting system for PD symptom identification would support clinicians in making more robust PD diagnostic decisions. We propose to analyze Parkinson's tremor (PT) to support the analysis of PD, since PT is one of the most typical symptoms of PD with broad generalizability. To realize the idea, we present SPA-PTA, a deep learning-based PT classification and severity estimation system that takes consumer-grade videos of front-facing humans as input. The core of the system is a novel attention module with a lightweight pyramidal channel-squeezing-fusion architecture that effectively extracts relevant PT information and filters noise. It enhances modeling performance while improving system interpretability. We validate our system via individual-based leave-one-out cross-validation on two tasks: the PT classification task and the tremor severity rating estimation task. Our system presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT class, while providing a 76.4% accuracy and 76.7% F1-score in more complex multiclass tremor rating classification task. Our system offers a cost-effective PT classification and tremor severity estimation results as warning signs of PD for undiagnosed patients with PT symptoms. In addition, it provides a potential solution for supporting PD diagnosis in regions with limited clinical resources.

A novel model for predicting prognosis in patients with idiopathic pulmonary fibrosis based on endoplasmic reticulum stress-related genes.

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic disease of unknown pathogenic origin. Endoplasmic reticulum (ER) stress refers to the process by which cells take measures to ER function when the morphology and function of the reticulum are changed. Recent studies have demonstrated that the ER was involved in the evolution and progression of IPF. In this study, we obtained transcriptome data and relevant clinical information from the Gene Expression Omnibus database and conducted bioinformatics analysis. Among the 544 ER stress-related genes (ERSRGs), 78 were identified as differentially expressed genes (DEGs). These DEGs were primarily enriched in response to ER stress, protein binding, and protein processing. Two genes (HTRA2 and KTN1) were included for constructing an accurate molecular signature. The overall survival of patients was remarkably worse in the high-risk group than in the low-risk group. We further analyzed the difference in immune cells between high-risk and low-risk groups. M0 and M2 macrophages were significantly increased in the high-risk group. Our results suggested that ERSRGs might play a critical role in the development of IPF by regulating the immune microenvironment in the lungs, which provide new insights on predicting the prognosis of patients with IPF.

Cancer screening programs in South-east Asia and Western Pacific

BackgroundThe burden of cancer can be altered by screening. The field of cancer screening is constantly evolving; from the initiation of program for new cancer types as well as exploring innovative screening strategies (e.g. new screening tests). The aim of this study was to perform a landscape analysis of existing cancer screening programs in South-East Asia and the Western Pacific.MethodsWe conducted an overview of cancer screening in the region with the goal of summarizing current designs of cancer screening programs. First, a selective narrative literature review was used as an exploration to identify countries with organized screening programs. Second, representatives of each country with an organized program were approached and asked to provide relevant information on the organizations of their national or regional cancer screening program.ResultsThere was wide variation in the screening strategies offered in the considered region with only eight programs identified as having an organized design. The majority of these programs did not meet all the essential criteria for being organized screening. The greatest variation was observed in the starting and stopping ages.ConclusionsEssential criteria of organized screening are missed. Improving organization is crucial to ensure that the beneficial effects of screening are achieved in the long-term. It is strongly recommended to consider a regional cancer screening network.

Infusing factual knowledge into pre-trained model for finding the contributions from the research articles

The growing volume of scientific literature makes it difficult for researchers to identify the key contributions of a research paper. Automating this process would facilitate efficient understanding, faster literature surveys and comparisons. The automated process may help researchers to identify relevant and impactful information in less time and effort. In this article, we address the challenge of identifying the contributions in research articles. We propose a method that infuses factual knowledge from a scientific knowledge graph into a pre-trained model. We divide the knowledge graph into mutually exclusive subgroups and infuse the knowledge in the pre-trained model using adapters. We also construct a scientific knowledge graph consisting of 3,600 Natural Language Processing (NLP) papers to acquire factual knowledge. In addition, we annotate a new test set to evaluate the model’s ability to identify sentences that make significant contributions to the papers. Our model achieves the best performance in comparison to previous methods with a relative improvement of 40.06% and 25.28% in terms of F1 score for identifying contributing sentences in the NLPContributionGraph (NCG) test set and the newly annotated test set, respectively.

Pharmacogenomic biomarker information on drug labels of the Spanish Agency of Medicines and Sanitary products: evaluation and comparison with other regulatory agencies.

This work aimed to analyse the pharmacogenetic information in the Spanish Drug Regulatory Agency (AEMPS) Summary of Products Characteristics (SmPC), evaluating the presence of pharmacogenetic biomarkers, as well as the associated recommendations. A total of 55.4% of the 1891 drug labels reviewed included information on pharmacogenetic biomarker(s). Pharmacogenomic information appears most frequently in the "antineoplastic and immunomodulating agents", "nervous system", and "cardiovascular system" Anatomical Therapeutic Chemical groups. A total of 509 different pharmacogenetic biomarkers were found, of which CYP450 enzymes accounted for almost 34% of the total drug-biomarker associations evaluated. A total of 3679 drug-biomarker pairs were identified, 102 of which were at the 1A level (PharmGKB® classification system), and 33.33% of these drug-pharmacogenetic biomarker pairs were assigned to "actionable PGx", 12.75% to "informative PGx", 4.9% to "testing recommended", and 4.9% to "testing required". The rate of coincidence in the assigned PGx level of recommendation between the AEMPS and regulatory agencies included in the PharmGKB® Drug Label Annotations database (i.e., the FDA, EMA, SWISS Medic, PMDA, and HCSC) ranged from 45% to 65%, being 'actionable level' the most frequent. On the other hand, discrepancies between agencies did not exceed 35%. This study highlights the presence of relevant pharmacogenetic information on Spanish drug labels, which would help avoid interactions, toxicity, or lack of treatment efficacy.

Whole genome sequencing and annotation of Daedaleopsis sinensis, a wood-decaying fungus significantly degrading lignocellulose.

Daedaleopsis sinensis is a fungus that grows on wood and secretes a series of enzymes to degrade cellulose, hemicellulose, and lignin and cause wood rot decay. Wood-decaying fungi have ecological, economic, edible, and medicinal functions. Furthermore, the use of microorganisms to biodegrade lignocellulose has high application value. Genome sequencing has allowed microorganisms to be analyzed from the aspects of genome characteristics, genome function annotation, metabolic pathways, and comparative genomics. Subsequently, the relevant information regarding lignocellulosic degradation has been mined by bioinformatics. Here, we sequenced and analyzed the genome of D. sinensis for the first time. A 51.67-Mb genome sequence was assembled to 24 contigs, which led to the prediction of 12,153 protein-coding genes. Kyoto Encyclopedia of Genes and Genomes database analysis of the D. sinensis data revealed that 3,831 genes are involved in almost 120 metabolic pathways. According to the Carbohydrate-Active Enzyme database, 481 enzymes are found in D. sinensis, of which glycoside hydrolases are the most abundant. The genome sequence of D. sinensis provides insights into its lignocellulosic degradation and subsequent applications.

The Legal Implications of the Aviation Industry’s Entrance to the Metaverse

Background: Technological growth allows aviation companies to embrace practices and applications that improve their approaches. A concept that is fast gaining attention from firms in this area is the Metaverse. This technology, driven by Artificial Intelligence (AI), improves consumer services, particularly by allowing passengers to travel virtually. Various entities already use this feature, and organisational and scholarly reports suggest that such establishments record positive outcomes. The primary goal of this analysis is to describe why operators must watch out for possible legal implications of using this tool. An important point is that they must prevent data security breaches that might violate consumers’ privacy rights. A few enterprises in this sector have become victims of infringements that resulted in data loss. Subsequently, some of these issues may proceed to court, and organisations spend many resources handling such cases. Another vital message relating to the utilisation of this innovation is that it could cause unfair competition. Particular establishments, especially those yet to deploy this idea, may claim groups that use Metaverse for exposing vital personal data to cyber attackers. Besides, the sector witnesses legal proceedings whereby some airlines blame competitors for indulging in unfair competition. While no specific Metaverse laws exist, a suitable remedy for operators is to follow legislations and policies that define AI use for commercial purposes. It is necessary to abide by regulations safeguarding consumers’ data privacy. Another solution is that corporations can adhere to international provisions such as the General Data Protection Regulation (GDPR) that have a global effect. Moreover, non-compliance could cause devastating legal repercussions that harm business practices. This paper introduces these challenges and pays more attention to the practical and legal aspects. Methods: This paper retrieves data from secondary sources, encompassing websites and journal articles. The approach entails reviewing what the authors of selected works present about the topic and taking relevant information for this project. The approach saves time and is cost-effective. Results and conclusion: Various firms in the aviation sector already use Metaverse to enhance their consumer experience. Companies feel attracted because of the many merits associated with the technology. However, they must watch out for the potential limitations of using this concept. In addition, users should consider the legal aspects of the innovation.

The association between healthcare expenditures and potentially inappropriate medication use in hospitalized older adults in Ethiopia

BackgroundEvidence shows that potentially inappropriate medication (PIM) use in older adults significantly increases the utilization of healthcare resources. PIM is widely prescribed in older adults, however little is known about its association with healthcare resource utilization in Africa, particularly Ethiopia. Therefore, the primary aim of this study was to examine the presence of an association between healthcare expenditure and the frequency of PIM used.MethodsThis observational study was conducted from 06 September 2021 to 30 December 2022. A total of 151 hospitalized older adult patients were included in the study. The data collection format was designed to capture relevant information. STATA V.15.0 was used for analysis. Descriptive statistics and a generalized linear model regression were conducted. Statistical significance was set at a p-value < 0.05. The findings are presented in tables, figures, and text.ResultsThe total healthcare expenditure was higher in PIM users (385,368.6 ETB) than in non-PIM users (131,267.7 ETB). The median expenditures for medical supplies (p = 0.025), investigations (p = 0.033), and total healthcare expenditure (p = 0.023) were significantly higher in patients with two and more PIMs than in those with no or one PIM. However, after model adjustment, the total healthcare expenditure was not significantly different across the frequency of PIMs used. Number of medications (adjusted B = 0.068, 95% CI: 0.035, 0.101, p < 0.001) and taking blood and blood-forming organ class of medication (adjusted B = 0.151, 95% CI: 0.005, 0.297, p = 0.042) were associated with higher total healthcare expenditure, whereas the total healthcare expenditure was significantly lower in those patients with diseases of the nervous system (adjusted B = -0.307, 95% CI: -0.502, -0.112, p = 0.002) and respiratory system (adjusted B = -0.196, 95% CI: -0.327, -0.065, p = 0.003).ConclusionThe total healthcare expenditure is nearly three times higher in PIM users. However, its association with the frequency of PIM use was not statistically significant in the final model. Deprescribing medications after evaluating the benefit-risk ratio may help to reduce the healthcare expenditures of older adult patients. Further similar, adequately powered, comparative study is also warranted to identify the actual effect of PIM use on healthcare expenditures in the local context.

Equine Rotavirus A under the One Health Lens: Potential Impacts on Public Health.

Group A rotaviruses are a well-known cause of viral gastroenteritis in infants and children, as well as in many mammalian species and birds, affecting them at a young age. This group of viruses has a double-stranded, segmented RNA genome with high genetic diversity linked to point mutations, recombination, and, importantly, reassortment. While initial molecular investigations undertaken in the 1900s suggested host range restriction among group A rotaviruses based on the fact that different gene segments were distributed among different animal species, recent molecular surveillance and genome constellation genotyping studies conducted by the Rotavirus Classification Working Group (RCWG) have shown that animal rotaviruses serve as a source of diversification of human rotavirus A, highlighting their zoonotic potential. Rotaviruses occurring in various animal species have been linked with contributing genetic material to human rotaviruses, including horses, with the most recent identification of equine-like G3 rotavirus A infecting children. The goal of this article is to review relevant information related to rotavirus structure/genomic organization, epidemiology (with a focus on human and equine rotavirus A), evolution, inter-species transmission, and the potential zoonotic role of equine and other animal rotaviruses. Diagnostics, surveillance and the current status of human and livestock vaccines against RVA are also reviewed.

Semantic and Context Understanding for Sentiment Analysis in Hindi Handwritten Character Recognition Using a Multiresolution Technique

The rapid growth of Web 2.0, which enables people to generate, communicate, and share information, has resulted in an increase in the total number of users. In developing countries, online users’ sentiment influences decision-making, social views, individual consumption decisions, and entity quality monitoring. As a result, more accurate sentiment analysis, particularly in their native language such as Hindi, is preferred over crude binary categorization. This is because of the abundance of web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. Analyzing this data and recovering valuable and relevant information from handwritten text has become extremely important. Despite years of research and development, no optical writing recognition (OCR) system has ever been certified as completely reliable. The first step in any pattern recognition system is feature selection. In many fields, feature selection is studied as a combinatorial optimization problem. The primary goal of feature selection is to reduce the number of redundant and ineffective traits in the recognition system. This feature selection is used to maintain or improve the performance of the classifier used by the recognition system: A support vector machine (SVM) technique could be used to solve this character recognition problem. The Hindi character recognition system recognizes Hindi characters by employing morphological operations, edge detection, HOG feature extraction, and an SVM-based classifier. The proposed model outperformed the current state-of-the-art method, achieving an accuracy of 96.77%.

Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.

Information regarding opioid use disorder (OUD) status and severity is important for patient care. Clinical notes provide valuable information for detecting and characterizing problematic opioid use, necessitating development of natural language processing (NLP) tools, which in turn requires reliably labeled OUD-relevant text and understanding of documentation patterns. To inform automated NLP methods, we aimed to develop and evaluate an annotation schema for characterizing OUD and its severity, and to document patterns of OUD-relevant information within clinical notes of heterogeneous patient cohorts. We developed an annotation schema to characterize OUD severity based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. In total, 2 annotators reviewed clinical notes from key encounters of 100 adult patients with varied evidence of OUD, including patients with and those without chronic pain, with and without medication treatment for OUD, and a control group. We completed annotations at the sentence level. We calculated severity scores based on annotation of note text with 18 classes aligned with criteria for OUD severity and determined positive predictive values for OUD severity. The annotation schema contained 27 classes. We annotated 1436 sentences from 82 patients; notes of 18 patients (11 of whom were controls) contained no relevant information. Interannotator agreement was above 70% for 11 of 15 batches of reviewed notes. Severity scores for control group patients were all 0. Among noncontrol patients, the mean severity score was 5.1 (SD 3.2), indicating moderate OUD, and the positive predictive value for detecting moderate or severe OUD was 0.71. Progress notes and notes from emergency department and outpatient settings contained the most and greatest diversity of information. Substance misuse and psychiatric classes were most prevalent and highly correlated across note types with high co-occurrence across patients. Implementation of the annotation schema demonstrated strong potential for inferring OUD severity based on key information in a small set of clinical notes and highlighting where such information is documented. These advancements will facilitate NLP tool development to improve OUD prevention, diagnosis, and treatment.

AuNPs/CNC Nanocomposite with A "Dual Dispersion" Effect for LDI-TOF MS Analysis of Intact Proteins in NSCLC Serum Exosomes.

Detecting exosomal markers using laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS) is a novel approach for examining liquid biopsies of non-small cell lung cancer (NSCLC) samples. However, LDI-TOF MS is limited by low sensitivity and poor reproducibility when analyzing intact proteins directly. In this report, gold nanoparticles/cellulose nanocrystals (AuNPs/CNC) is introduced as the matrix for direct analysis of intact proteins in NSCLC serum exosomes. AuNPs/CNC with "dual dispersion" effects dispersed and stabilized AuNPs and improved ion inhibition effects caused by protein aggregation. These features increased the signal-to-noise ratio of [M+H]+ peaks by two orders of magnitude and lowered the detection limit of intact proteins to 0.01mgmL-1. The coefficient of variation with or without AuNPs/CNC is measured as 10.2% and 32.5%, respectively. The excellent reproducibility yielded a linear relationship (y=15.41x - 7.983, R2=0.989) over the protein concentration range of 0.01 to 20mgmL-1. Finally, AuNPs/CNC-assisted LDI-TOF MS provides clinically relevant fingerprint information of exosomal proteins in NSCLC serum, and characteristic proteins S100 calcium-binding protein A10, Urokinase plasminogen activator surface receptor, Plasma protease C1 inhibitor, Tyrosine-protein kinase Fgr and Mannose-binding lectin associated serine protease 2 represented excellent predictive biomarkers of NSCLC risk.

Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach

Traffic accidents have a substantial impact on human life and property, resulting in millions of injuries every year. To ensure road safety and enhance the research in this direction, it is necessary to develop methods that can efficiently predict and classify the accident severity. However, traffic accident datasets may contain a large number of features, making it challenging to extract relevant information and patterns from high-dimensional data. Moreover, traffic accidents may be influenced by multiple factors and temporal dependencies, leading to a dynamic impact of each factor on accident severity over time. To address these challenges, we propose a novel deep-learning approach for predicting traffic accident severity. Specifically, we first conduct a thorough data preprocessing step to clean the data and ensure its quality. Then, a Convolutional Neural Network (CNN) is introduced to extract spatial features and patterns from the high-dimensional data, followed by a Bidirectional Long Short-Term Memory network (BiLSTM) to capture the temporal dependencies between various factors that affect traffic accidents. We also implement attention mechanisms to weigh the importance of each feature in the prediction, thereby reducing the impact of noisy or irrelevant data. To evaluate the effectiveness of our approach, we conduct experiments on a real-world traffic accident dataset from two cities. The results demonstrate the practicality and effectiveness of our framework for traffic accident severity prediction, with potential to enhance road safety.

Generation of Architectural and Other Safety Measures Against Crises and Killings at Residential Environments in Plateau State of Nigeria

Over the years there have been series of crisis in Plateau State of Nigeria and they have led to massive killings and destruction of valuable properties; this is a very serious problem. Hence, it became necessary to conduct a research work with the aim of overviewing the cases of crises and killings at residential environments in Plateau State, in order to generate architectural and other safety measures to reduce their future occurrences in Nigeria. The research focused on desktop study as the instrument for data collection; reviewed different newspapers in Nigeria with available crisis and killing cases in Plateau State, in order to get relevant information to the research. Thus, table was used for the presentation and analysis of the research data, so as to generate results and the results were checked by using contents analysis technique, in order to validate them. Among the architectural research findings is: 1,290 houses were burnt during December, 2023 crisis in Bokkos Local Government Area of Plateau State and some other research findings are: the security agencies knew that an attack was coming before the 2023 Christmas eve massacre; the security forces know the attackers, and they even know their hideouts for two decades. Among the recommended architectural safety measures to reduce future crises and killings is: the design of the members of roof, door, window, ceiling and any other part of building that can easily burn with the use of wood should be fire retardant type, in order to reduce death, massive destruction of houses and valuable properties in times of fire attack from the terrorists. Some other recommended safety measures are: there should be advanced training of security agencies on immediate defensive responses against the terrorists been rumored of their coming to the state; urgent investigations must be made on the security forces that knew the attackers and their hideouts for two decades without revealing the information, so that the defaulted members of those security forces can receive a very severe/appropriate punishment, in order to stop this kind of bad service in Nigeria.

The Role of Public Relations in Disseminating Human Rights Culture and Social Peace in the Iraqi Society An Analytical Study of the Facebook Page of the High Commission for Human Rights

This research aims to clarify the role of public relations in disseminating a culture of human rights and social peace among the Iraqi public by examining the activities of the Public Relations Department in the Higher Commission for Human Rights. The primary focus of the research revolves around the question, "What is the role of public relations in spreading a culture of human rights and social peace among the Iraqi public?" The researcher employed a survey methodology to analyze the commission's activities published on its official Facebook page using both quantitative and qualitative content analysis from 1/10/2020, to 1/10/2021. Additionally, the researcher conducted scientific interviews with employees in the Public Relations and Outreach departments to obtain relevant information and data for the research. The study found that field visits were among the most important direct communication activities, while press releases constituted indirect communication activities, focusing on topics such as the right to healthcare and social care for citizens. The commission utilized persuasion techniques and arguments to convince its audience of the content of its communication messages. The communication strategies were identified as media strategy, relationship-building strategy, dialogue strategy, and social responsibility strategy. The research yielded several results, including the commission's focus on human rights in general and specific rights for targeted groups. Notably, field visits, receiving appeals and complaints, and conducting training workshops were prominent activities. Various persuasive techniques were applied, with a focus on logical appeals achieving the highest percentage compared to emotional and fear appeals. Additionally, the commission emphasized the use of logical arguments, such as the precedent argument, causal argument, and historical argument in its diverse content. Keywords: Role, Public Relations, culture, Human Rights, Social Peace, Higher Commission for Human Rights.