Irritable Bowel Syndrome among Medical and Non-Medical Northern Border University Students, Kingdom of Saudi Arabia: Across Sectional Study

Background: Irritable Bowel Syndrome (IBS) is a common functional gastrointestinal disorder (FGID), characterized by abdominal pain or discomfort and alteration in bowel habits. Aim of the study: To determine the overall prevalence, prevalence of each type and risk factors of IBS among Northern Border University (NBU) students, Arar, Kingdom of Saudi Arabia. Material and methods: We use cross sectional, descriptive study with multistage cluster probability sample. Using Rome III criteria questionnaire of IBS;which is a self-administrated consists of ten questions assessing the current status of an apparently normal person. The questionnaire is administrated to Northern Border University students. Results: A total of 228 University students of them, 94 (41.2%) males and 134 (58.8%) females were included in the study. The overall prevalence of IBS according to Rome III criteria in northern border University was (32.5%). The disease prevalence was 33.6% in females and 30.9% in males. Among the study participants, the most common type of IBS was the mixed one 12.7%, followed by the constipation predominant type 10.5%, then the diarrhea pre-dominant type 5.7% while the least common was unsubtyped cases (3.5%). Statistically significant increase in prevalence of this disease was found among female students (60.8% vs. 39.2% in males) (p-value < 0.05), the students who experienced psychic stress and irritability (79.7%) (p-value < 0.05) and students who were obese (p-value < 0.001). Conclusion: The results of this study concluded the prevalence rate of 32.5% for IBS among the students studying in Northern Border University. Stress and high body mass index were significantly associated with IBS. In addition, this study concluded that IBS was not significantly associated with socio-demographic characteristics and smoking.

Advanced Functional Electromagnetic Shielding Materials:A Review Based on Micro‑Nano Structure Interface Control of Biomass Cell Walls

Research efforts on electromagnetic interference(EMI)shielding materials have begun to converge on green and sustainable biomass materials.These materials offer numerous advantages such as being lightweight,porous,and hierarchical.Due to their porous nature,interfacial compatibility,and electrical conductivity,biomass materials hold significant potential as EMI shielding materials.Despite concerted efforts on the EMI shielding of biomass materials have been reported,this research area is still relatively new compared to traditional EMI shielding materials.In particular,a more comprehensive study and summary of the factors influencing biomass EMI shielding materials including the pore structure adjustment,preparation process,and micro-control would be valuable.The preparation methods and characteristics of wood,bamboo,cellulose and lignin in EMI shielding field are critically discussed in this paper,and similar biomass EMI materials are summarized and analyzed.The composite methods and fillers of various biomass materials were reviewed.this paper also highlights the mechanism of EMI shielding as well as existing prospects and challenges for development trends in this field.

Chemical diversity of essential oils from flowers,leaves,and stems of Rhanterium epapposum Oliv. growing in northern border region of Saudi Arabia

Objective:To evaluate the medicinal uses of Rhanterium epapposum Oliv.(R.epapposum) growing in northern border region of Saudi Arabia,through the chemical diversity of essential oils extracted from its flowers,leaves and stems.Methods:Aerial parts of R.epapposum were collected in April 2014.Air dried flowers,leaves,and stems were separately subjected to hydrodistillation in a Clevenger-type apparatus for 4 h to extract the essential oils.Gas chromatography-mass spectrometry analysis of the essential oils was carried out using an Agilent 6890 gas chromatograph equipped with an Agilent 5973 mass spectrometric detector.Results:A total of 51 compounds representing 76.35%–94.86% of flowers,leaves and stems oils composition were identified.The chemical profiles of the studied fractions revealed the dominance of monoterpenes,regardless of qualitative and quantitative differences observed.Limonene,linalool,4-terpineol and a-cadinol represented the major constituents of flowers oil.Leaves oil was dominated by limonene,sabinene,a-pinene and b-myrcene whereas linalool,ionole,a-cadinol,b-eudesmol,4-terpineol,and aterpineol were the major constituents of stems oil.Conclusions:Essential oils from flowers,leaves and stems of R.epapposum growing in northern border region of Saudi Arabia are considered as a rich source of monoterpenes which have biological activities.

Mineralogy,Chemical Characteristics and Upgrading of Beach Ilmenite of the Top Meter of Black Sand Deposits of the Kafr Al-Sheikh Governorate,Northern Egypt

Egyptian beach ilmenite occurs in a relatively high content in the naturally highly concentrated superficial black sand deposits at specific beach zones in the northern parts of the Nile Delta at Rosetta. Microscopic study shows that the ilmenite occurs as fresh homogeneous black or heterogeneous multicoloured altered grains and exhibits three types （homogeneous, exsolved and altered） of ilmenite varieties. XRD data of ilmenite indicates their association with minor hematite and quartz, whereas leucoxene shows its association with Nb-rutUe, pseudorutile and hematite. Grain size distribution suggests a very fine sand size of 〉89% and 80% and a fine sand size of 10.5% and 18.3% for fresh and altered ilmenites, respectively. The density of fresh, altered ilmenite and leucoxene concentrates varies from 2.70, 2.50 to 2.40 ton/m^3, suggesting a gradual decrease from high grade fresh to leucoxene and consistent with variation in magnetic susceptibility as a consequence of the leaching of iron. Mass magnetic susceptibility reveals 97.6% of ilmenite and 92% of the altered form are obtained at 0.20 and 0.48 ampere. Fresh iimenite exhibits variable TiO2 （47.18%） and Fe2O3^T （46.10%） with minor MnO, MgO and Cr2O3 （1.22, 1.10 and 0.51%）. The altered ilmenite is higher in TiO2 （76.16%） and SiO2 （4.68%） and lower in Fe2O3^T （14.45%）, MnO, MgO and Cr2O3 （0.39, 0.52 and 0.11%） compared with the fresh form. Three concentrates of ilmenites （G1, G2 and G3） were prepared from crude ore using a Reading cross belt magnetic separator under different conditions, revealing a gradual increase of TiO2, SiO2, Al2O3 and CaO accompanied by a decrease of Fe2O3^T, MgO and Cr2O3 with repetition of the separation processes. Several ore dressing techniques were carried out to upgrade the iimenite concentrate.

Dietary Habits and Oral Hygiene Practice amongst Dental Students at the College of Dentistry, Princess Nourah University

The aim of the present study was to assess the dietary habits and oral hygiene practice of dental students in a new dental school. A self-administered structured closed-ended questionnaire on demographic characteristics, medical history, oral hygiene and dietary habits was distributed to dental students. Results showed that One third of students indicated that they don’t consume low pH beverages (soft drinks) at all, while 48.9% drink a soft drink or two a day. Students took varying amount of time to consume their drinks. The majority of participants consumed citric juices, fruits and/or pickles at least once a day. 91.3% of students use either soft (41.8%) or medium (49.5%) toothbrush. Only a fifth (16.9%) of the students brush their teeth after drinking soft drinks and 58.2% brush their teeth after vomiting. In conclusion, young adults need to be aware about their dietary habits & oral hygiene, and also a proper dental health program needs to be applied.

MXene@c-MWCNT Adhesive Silica Nanofiber Membranes Enhancing Electromagnetic Interference Shielding and Thermal Insulation Performance in Extreme Environments

A lightweight flexible thermally stable composite is fabricated by com-bining silica nanofiber membranes(SNM)with MXene@c-MWCNT hybrid film.The flexible SNM with outstanding thermal insulation are prepared from tetraethyl orthosilicate hydrolysis and condensation by electrospinning and high-temperature calcination;the MXene@c-MWCNT_(x:y)films are prepared by vacuum filtration tech-nology.In particular,the SNM and MXene@c-MWCNT_(6:4)as one unit layer(SMC_(1))are bonded together with 5 wt%polyvinyl alcohol(PVA)solution,which exhibits low thermal conductivity(0.066 W m^(-1)K^(-1))and good electromagnetic interference(EMI)shielding performance(average EMI SE_(T),37.8 dB).With the increase in func-tional unit layer,the overall thermal insulation performance of the whole composite film(SMC_(x))remains stable,and EMI shielding performance is greatly improved,especially for SMC_(3)with three unit layers,the average EMI SET is as high as 55.4 dB.In addition,the organic combination of rigid SNM and tough MXene@c-MWCNT_(6:4)makes SMC_(x)exhibit good mechanical tensile strength.Importantly,SMC_(x)exhibit stable EMI shielding and excellent thermal insulation even in extreme heat and cold environment.Therefore,this work provides a novel design idea and important reference value for EMI shielding and thermal insulation components used in extreme environmental protection equipment in the future.

AI-Based Helmet Violation Detection for Traffic Management System

Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledging the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning methodologies.The primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection capabilities.Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing benchmarks.Furthermore,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.

AMAD:Adaptive Mapping Approach for Datacenter Networks,an Energy-Friend Resource Allocation Framework via Repeated Leader Follower Game

Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict their utility objectives.Yet,besides the cost of the physical assets and network resources,such scaling may also imposemore loads on the electricity power grids to feed the added nodes with the required energy to run and cool,which comes with extra costs too.Thus,those CDNproviders who utilize their resources better can certainly afford their services at lower price-units when compared to others who simply choose the scaling solutions.Resource utilization is a quite challenging process;indeed,clients of CDNs usually tend to exaggerate their true resource requirements when they lease their resources.Service providers are committed to their clients with Service Level Agreements(SLAs).Therefore,any amendment to the resource allocations needs to be approved by the clients first.In this work,we propose deploying a Stackelberg leadership framework to formulate a negotiation game between the cloud service providers and their client tenants.Through this,the providers seek to retrieve those leased unused resources from their clients.Cooperation is not expected from the clients,and they may ask high price units to return their extra resources to the provider’s premises.Hence,to motivate cooperation in such a non-cooperative game,as an extension to theVickery auctions,we developed an incentive-compatible pricingmodel for the returned resources.Moreover,we also proposed building a behavior belief function that shapes the way of negotiation and compensation for each client.Compared to other benchmark models,the assessment results showthat our proposed models provide for timely negotiation schemes,allowing for better resource utilization rates,higher utilities,and grid-friend CDNs.

Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and Friedman.It articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility patterns.The study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network scenarios.The findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network requirements.This paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

Optimizing Connections:Applied Shortest Path Algorithms for MANETs

This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.

Sports Events Recognition Using Multi Features and Deep Belief Network

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or conditions.This recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial intelligence.This research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those people.Common approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton points.Moreover,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or silhouettes.The integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire procedure.These extracted features are passed to the early fusion and DBscan for feature fusion and optimization.Then deep belief,network is employed for recognition.Experimental results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

Enhanced Object Detection and Classification via Multi-Method Fusion

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes.Various technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene comprehension.This paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an image.The methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet architecture.Afterward,we use an annotation tool to accurately label the segmented regions.Upon labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob characteristics.For the classification stage,a convolution neural network(CNN)is employed.This comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in images.The experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been documented.After analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

Design, mechanism, delivery and therapeutics of canonical and Dicer-substrate siRNA

Upon the discovery of RNA interference(RNAi),canonical small interfering RNA(si RNA) has been recognized to trigger sequence-specific gene silencing. Despite the benefits of si RNAs as potential new drugs,there are obstacles still to be overcome,including off-target effects and immune stimulation. More recently,Dicer substrate si RNA(Dsi RNA) has been introduced as an alternative to si RNA. Similarly,it also is proving to be potent and target-specific,while rendering less immune stimulation. Dsi RNA is 25–30 nucleotides in length,and is further cleaved and processed by the Dicer enzyme. As with si RNA,it is crucial to design and develop a stable,safe,and efficient system for the delivery of Dsi RNA into the cytoplasm of targeted cells. Several polymeric nanoparticle systems have been well established to load Dsi RNA for in vitro and in vivo delivery,thereby overcoming a major hurdle in the therapeutic uses of Dsi RNA. The present review focuses on a comparison of si RNA and Dsi RNA on the basis of their design,mechanism,in vitro and in vivo delivery,and therapeutics.

A Secure Communication Protocol for Unmanned Aerial Vehicles

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles.Multiple UAVs and autopilot systems support it,and it provides bi-directional communication between the UAV and Ground Control Station.The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS.In order to increase the transfer speed and efficiency,the Mavlink does not encrypt the messages.As a result,the protocol is vulnerable to various security attacks such as Eavesdropping,GPS Spoofing,and DDoS.In this study,we tackle the problem and secure the Mavlink communication protocol.By leveraging the Mavlink packet’s vulnerabilities,this research work introduces an experiment in which,first,the Mavlink packets are compromised in terms of security requirements based on our threat model.The results show that the protocol is insecure and the attacks carried out are successful.To overcomeMavlink security,an additional security layer is added to encrypt and secure the protocol.An encryption technique is proposed that makes the communication between the UAV and GCS secure.The results show that the Mavlink packets are encrypted using our technique without affecting the performance and efficiency.The results are validated in terms of transfer speed,performance,and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink protocol.Our achieved results have significant improvement over the literature and Mavlink in terms of security.

Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater objects.Therefore,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic technologies.These communication technologies are already used for communication in the underwater environment;however,lacking localization solutions.Optical and magnetic induction communication achieves higher data rates for short communication.On the contrary,acoustic waves provide a low data rate for long-range underwater communication.The proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final locations.Moreover,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

Dimethylether production on zeolite catalysts activated by Cl^-,F^- and / or ultrasonication

The chlorinated and fluorinated zeolite catalysts were prepared by the impregnation of zeolites( H-ZSM-5,H-MOR or H-Y) using two halogen precursors( ammonium chloride and ammonium fluoride) in this study. The influence of ultrasonic irradiation was evaluated for optimizing both halogen precursors for production of dimethylether( DME) via methanol dehydration in a fixed bed reactor. The catalysts were characterized by SEM,XRD,BET and NH3-TPD. The reaction conditions were temperatures from 100 to 300 ℃ and a WHSV = 15. 9 h-1. All halogenated catalysts showhigher catalytic activities at all reaction temperatures studied. However, the halogenated zeolite catalysts prepared under ultrasonic irradiation showhigher performance for DME formation. The chlorinated zeolite catalysts show higher activity and selectivity for DME production than the respective fluorinated versions.