Recent publications
Under the constraint of carbon neutrality targets, the issue of energy poverty has garnered increasing concerns. This study aims to measure energy poverty and assess the impact of renewable energy technology innovation in 30 provinces across China. Furthermore, it calculates city-level energy poverty in 274 cities. By examining the influence of renewable energy technology innovation on energy poverty, this study proposes a new approach to accelerate energy transition and promote sustainable development. The regression results, which have undergone rigorous robustness and endogeneity tests, demonstrate that renewable energy technology innovation significantly alleviates energy poverty. Moreover, the treatment effect of policies is determined through the regression control method. Specifically, the cities of Zhangjiakou, Baotou, and Qiqihar are analyzed to provide targeted suggestions for China’s energy development. The treatment effects of both provincial and city-level policies indicate that implementing renewable energy demonstration zones can effectively alleviate energy poverty.
JEL Classification: Q48; O31
This bibliometric analysis endeavors to bridge a gap in existing research on social support for the older adults, focusing on the theme of ageing. The primary objective is to provide a comprehensive overview of scholarly advancement of previous research papers in this domain, employing a bibliometric approach. A dataset inclusive of publications up to February 2024 from Scopus database has been compiled, capturing pertinent information on older adults’ social support. Employing both quantitative and qualitative methodologies, the study offers a historical panorama of research trends. Through bibliometric techniques, significant articles, authors, journals, organizations, and countries contributing to this field are identified. The analysis unveils the current research status, elucidating key contributors, influential publications, and emerging thematic trajectories within ageing studies. Citation patterns and literature examination aid in identifying influential factors shaping the scholarly landscape. This research significantly enhances understanding of formal and informal social support for the older adults, filling existing research lacunae while spotlighting key contributors and burgeoning areas of interest. The findings of relevant studies based on PRISMA screening, a total of 152 research papers were screened. Seventeen papers were excluded because they did not specifically address social support for older adults. Three review papers and four others were removed due to being out of scope. Finally, 128 documents were retained for bibliometric analysis. The publication trend stands at 3.73%, indicating a significant increase in scholarly interest in social support for older adults.
Streptococcus pneumoniae (S. pneumoniae), which is a Gram-positive diplococcus, has emerged as a significant human pathogen. It is a primary cause of bacterial pneumonia, otitis media, meningitis, and septicemia, leading to a considerable impact on global morbidity and mortality. The investigation of S. pneumoniae and its virulence factors has resulted in the identification of surface endonuclease A (EndA). EndA functions in DNA uptake during natural transformation and plays a significant role in gene transfer. The ability of S. pneumoniae to degrade neutrophil extracellular traps (NETs) enhances its virulence and invasive potential in pneumococcal infections. NETosis occurs when neutrophils release chromatin into the extracellular space to form NETs, capturing and neutralizing pathogens. Currently, NETosis can be induced by several microbes, particulate matter, and sterile stimuli through distinct cellular mechanisms, and this includes the involvement of EndA in S. pneumoniae. Here, we reviewed the cellular functions of EndA, its role in S. pneumoniae as a virulence factor in relation to NETosis, its relationship to immunogenicity, and its involvement in several diseases. The discovery of this relationship would significantly impact therapeutic technology in reducing disease burden, especially pneumococcal infections.
Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.
Kidney function gradually declines as a result of chronic kidney disease (CKD). The current study was conducted at Princess Iman Hospital in Muadi, Jordan from December to March 2024. It aimed to investigate the association between lipids and chronic renal failure (CRF), which refers to the advanced stages of CKD where kidney function has declined significantly, and to understand how dyslipidemia affects the development of CKD and general health outcomes. The study involved three groups of participants: patients with CRF who were on hemodialysis, those receiving conservative management for CRF, and healthy individuals as controls. According to the findings, CRF patients (hemodialysis and conservative management) had significantly higher lipid levels than the control group besides showing low indicators for kidney function (p<0.001). In addition, triglyceride, cholesterol, low-density lipoprotein (LDL) levels, Cholesterol/high-density lipoprotein (HDL) ratio, and LDL/HDL ratio were also found to be significantly high in the hemodialysis group when compared to the conservative group (p<0.001). In this population with CRFs, it was observed that lipid levels correlated positively with markers for kidney disease progression. Therefore, monitoring of lipids should be done regularly across all stages of CKDs to reduce cardiovascular complications associated with atherosclerosis. Hence, incorporating lipid evaluations into standard CKD care regimens, even during the initial phases, is vital for enhancing patient outcomes and lowering mortality risks. In essence, the results highlight the importance of proactive management of lipid levels in CKD individuals to tackle cardiovascular complications effectively. By understanding dyslipidemia's impact on CKD advancement, healthcare practitioners can customize interventions to enhance patient care and diminish related risks, ultimately improving prognosis and decreasing mortality rates among CKD cohorts.
Background and Aims
Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three‐dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis.
Methods
We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings.
Results and Conclusion
The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL‐based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation.
Chemotherapy and other traditional anticancer treatments are losing their efficacy in the battle against cancer. As a result, cancer treatment strategies must be continually adjusted to meet the rising demand for alternative medicines. Several viral and non-viral vectors have been used previously. However, it has been shown that microorganisms are a strong contender for successfully combating cancer. They are a remarkable source of toxins, polysaccharides, tumor-specific anticancer genes, nanodrugs and gene-delivery vectors. One of the emerging key players in cancer therapy is bacteria. It has been demonstrated that traditional methods of altering the microbiome, such as antibiotics, probiotics and microbiota transplants, can sometimes increase the effectiveness of cancer therapies. However, problems with these methods, such as consistency and collateral damage to the commensal microbiota, spur the development of new technologies specifically aimed at the microbiome-cancer interface. In light of nanotechnology’s success in transforming cancer diagnostics and treatment, nanotechnologies with the capacity to control interactions that occur across microscopic and molecular length scales in the microbiome and the tumor microenvironment have the potential to provide innovative methods for cancer treatment. The relationship between nanotechnology, the microbiome and cancer offers tremendous potential. This paper highlights the contributions of significant bacterial groups to several anticancer research fields.
Alzheimer’s disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provides the most helpful neuroimaging tool for detecting AD. In this paper, we employ a DenseNet-201 based transfer learning technique for diagnosing different Alzheimer’s stages as Non-Demented (ND), Moderate Demented (MOD), Mild Demented (MD), Very Mild Demented (VMD), and Severe Demented (SD). The suggested method for a dataset of MRI scans for Alzheimer’s disease is divided into five classes. Data augmentation methods were used to expand the size of the dataset and increase DenseNet-201’s accuracy. It was found that the proposed strategy provides a very high classification accuracy. This practical and reliable model delivers a success rate of 98.24%. The findings of the experiments demonstrate that the suggested deep learning approach is more accurate and performs well compared to existing techniques and state-of-the-art methods.
Traumatic damage to the spinal cord (SCI) frequently leads to irreversible neurological deficits, which may be related to apoptotic neurodegeneration in nerve tissue. The MLC901 treatment possesses neuroprotective and neuroregenerative activity. This study aimed to explore the regenerative potential of MLC901 and the molecular mechanisms promoting neurogenesis and functional recovery after SCI in rats. A calibrated forceps compression injury for 15 s was used to induce SCI in rats, followed by an examination of the impacts of MLC901 on functional recovery. The Basso, Beattie, and Bresnahan (BBB) scores were utilized to assess neuronal functional recovery; H&E and immunohistochemistry (IHC) staining were also used to observe pathological changes in the lesion area. Somatosensory Evoked Potentials (SEPs) were measured using the Nicolet® Viking Quest™ apparatus. Additionally, we employed the Western blot assay to identify PI3K/AKT/GSK-3β pathway-related proteins and to assess the levels of GAP-43 and GFAP through immunohistochemistry staining. The study findings revealed that MLC901 improved hind-limb motor function recovery, alleviating the pathological damage induced by SCI. Moreover, MLC901 significantly enhanced locomotor activity, SEPs waveform, latency, amplitude, and nerve conduction velocity. The treatment also promoted GAP-43 expression and reduced reactive astrocytes (GFAP). MLC901 treatment activated p-AKT reduced p-GSK-3β expression levels and showed a normalized ratio (fold changes) relative to β-tubulin. Specifically, p-AKT exhibited a 4-fold increase, while p-GSK-3β showed a 2-fold decrease in T rats compared to UT rats. In conclusion, these results suggest that the treatment mitigates pathological tissue damage and effectively improves neural functional recovery following SCI, primarily by alleviating apoptosis and promoting neurogenesis. The underlying molecular mechanism of this treatment mainly involves the activation of the PI3K/AKT/GSK-3β pathway.
According to the Quran, depression in general is attributed to a lack of religious commitment among Muslims, as well as the accumulation of sadness, anxiety, fear, disorder, psychological pressure, and issues related to family, social, cultural, economic, and political relationships. In the light of the challenges faced by the Yemeni community in Malaysia due to forced displacement from Yemen and the resulting lack of security from this traumatic experience and the inability to return, this study aims to uncover the nature, causes, and effects of psychological depression. Additionally, it seeks to identify proposals for alleviating symptoms based on the perspectives of a sample of 45 individuals from the Yemeni community in Malaysia. The researchers employed descriptive and analytical methods, and the study’s theoretical findings were consistent with field observations. The primary cause of depression was found to be fear for the well-being of loved ones, with significant impact stemming from anxiety and stress about the future. One of the key suggestions for alleviation was fostering certainty in future benefits.
The whole world is now widely using green energy compared to fossil because of the depletion of fossil fuels, the rising temperature of the earth, and changing weather conditions, all these things are becoming a big threat to the life of the earth. This study proposed a stand-alone hybrid renewable energy system using different types of batteries. This model includes photovoltaic arrays, wind turbines, diesel generators, converters, and batteries. Lead-acid and lithium-ion batteries have been compared for the selection of optimal battery based on hybrid renewable energy system and sustainable development requirements. The purpose of this study is to find the optimal configuration, and techno-economic characteristics, using the hybrid optimization of multiple energy resources technique. The results of Lithium-ion and Lead Acid have been compared and it is found that the best configuration is photovoltaic arrays/wind turbines/ diesel generators /Battery/converter with lithium-ion Batteries. The net present cost and cost of energy are found to be 1.64M and 0.144$ respectively, for the selected study location. The carbon dioxide emission for configuration with LI batteries is 107314 kg/year as against the LA batteries which have 351288 kg/year. The results show LI batteries are technically as well as economically better than the LA batteries.
The multilevel inverter (MLI) is a power electronic circuit applied for power and voltage applications. It has the advantages of minimum total harmonic distortion (THD), less voltage stress on the switching devices, fewer switching losses, and a smaller size of passive filters. These high-level inverters are applied in applications like AC drives, FACTS, and in the field of renewable energy. There are various inverters for such applications. A PUC multilevel inverter contains fewer components, fewer switching losses, and easy-to-balance voltage on capacitor sides. In this paper, the performance of the transformer less seven-level inverter and the five-level inverter is analyzed. The sinusoidal pulse width modulation (PWM) has been used with this PUC-based structure. The new control strategy is designed to reduce harmonic contents and low filter ratings. A relative analysis is carried out to focus on the superiority of the recently developed packed U-cell topology. The performance of packed U-cell, seven-level, and five-level inverters has been compared. These types of inverters consist of two power switches and a single capacitor to make this inverter effective. The PUC 7 inverter contains a lot of sensors and other control components to balance the voltage on the capacitor side by one-third of the input voltage, so the structure becomes complex. While PUC 5 is sensor less technology, there is no voltage balancing issue and no complexity found in this inverter. In PUC5, the only level is low, but it is very easy to interface with grids and other devices. So it is the most preferable as compared to PUC7. The performance analyses of these inverters have been verified through simulation.
The rise in illegal crude oil theft and refining in the southern Niger Delta region of Nigeria, especially in Rivers State, has led to significant environmental damage to aquatic ecosystems. A study was carried out to assess the impact of crude oil bunkering on aquatic environments and fish samples from Oproama, Sama-Naguakiri, and Abalama over six months. Findings revealed that Oproama had the highest levels of biological oxygen demand (3.60 ± 0.79 mg/L), electrical conductivity (34.07 ± 3.62 μS/cm), total dissolved solids (28.17 ± 3.77 mg/L), and temperature (29.50 ± 0.74 °C). In contrast, Sama-Naguakiri recorded the highest pH (6.72 ± 0.14) and dissolved oxygen (3.35 ± 0.11 mg/L). Though minor variances were noted between Sama-Naguakiri and Abalama, a significant difference (P < 0.05) was observed between these areas and Oproama. Importantly, all measured values adhered to WHO/FAO standards. Analysis of potentially harmful metals in sediment and water indicated notable distinctions among the three sites, with Sama-Naguakiri exhibiting the highest levels of Zn (114.5 ± 1.5 mg/kg), Cu (237.8 ± 0.9 mg/kg), Pb (3.6 ± 1.2 mg/kg), and Cd (1.1 ± 0.4 mg/kg). Conversely, Abalama showed the lowest zinc (105.2 ± 1.5 mg/kg) and lead (2.4 ± 0.5 mg/kg) concentrations, while Oproama displayed the lowest copper level (0.8 ± 0.3 mg/kg). The concentrations of heavy metals in the water, sediment, and fish surpassed the permissible limits established by NESREA, the EPA, and WHO, except for arsenic. The presence of heavy metals in this region could pose significant ecological and health hazards, underscoring the urgency for immediate remedial measures to safeguard the environment and this fish-dependent community.
This study aims to investigate the application of Maslahah Mursalah reasoning in contemporary Islamic finance. The motivation for this study arises from the modernization of Islamic banking practices, which have adapted conventional financial systems through structural and operational changes. Consequently, it is necessary to evaluate these practices from a Shariah law perspective. One of the principles of Usul al-Fiqh that is relevant for addressing modern issues not addressed in earlier times is the principle of Maslahah Mursalah. Maslahah Mursalah is a significant principle for determining the compliance of an action with Shariah. Furthermore, this principle is accepted as valid by renowned scholars among the four Sunni schools of thought. The objective of this study is to analyze the implementation of Maslahah Mursalah reasoning in current Islamic financial practices. A qualitative approach is employed, which includes a literature review of books, journals, and relevant circulars. The gathered materials will be analyzed using document analysis methods. The findings of this study indicate that several practices in Islamic finance are implemented based on the justification of Maslahah Mursalah. These practices include issues such as the inclusion of a 'promise to purchase' in Murabahah to Purchase Order (MPO) contracts, the determination of Ibra' (rebate) in financing contracts, and the combination of contracts in Islamic financial products. Therefore, it can be concluded that the principle of Maslahah Mursalah plays a significant role as a primary reference for contemporary Islamic finance issues. Consequently, it is recommended that operators in the current Islamic financial industry have a thorough understanding of the principle of Maslahah Mursalah.
Qiyas plays a crucial role in the formulation of Sharia law, being one of the primary sources of evidence used in the science of usul al-fiqh. Although usul al-fiqh, the science that discusses the process of deriving legal rulings, is a necessity for those involved in the issuance of legal rulings, it is observed that in the current context, the literature on usul al-fiqh has not received adequate attention, especially in the domain of Islamic finance. Therefore, one of the suitable sources of usul al-fiqh for resolving modern issues that were not addressed in ancient times is the source of qiyas. Qiyas is essential in determining the element of similarity from the perspective of the ‘illah (effective cause) of the law between an issue explicitly mentioned in the Sharia texts and a new issue. Furthermore, this source is accepted as evidence by the reputable scholars among the four Sunni schools of thought. This study aims to analyze the application of qiyas as evidence in contemporary Islamic financial practices. The study employs a qualitative approach, involving a literature review encompassing books, journals, and relevant circulars. The collected materials are analyzed using document analysis methods. The findings of the study indicate that several practices in Islamic finance are implemented based on the justification of qiyas. These practices include issues related to fiat currency, the requirement for a minimum amount of physical assets in sukuk, the zakat on jewelry (gold and silver), and the zakat on publicly listed company shares. Therefore, it can be concluded that qiyas plays an important role as a primary reference for contemporary issues in Islamic finance. It is recommended that current Islamic finance industry practitioners thoroughly understand the source of qiyas.
Hashimoto’s thyroiditis (HT) is an autoimmune disorder characterized by elevated thyroid-stimulating hormone (TSH) levels. This research investigates the complex interaction between HT and cardiovascular risk in adult Jordanian non-pregnant women aged 20-50. Through a study involving 50 HT subjects and 40 healthy subjects, the levels of lipoprotein-associated phospholipase A2 (PLA2), high sensitivity C-reactive protein (hs-CRP), and anti-thyroid peroxidase (anti-TPO) antibodies were compared using ELISA methods and enzymatic colorimetric assays for lipid profiles. The results revealed significantly higher serum levels of hs-CRP, PLA2, and Anti-TPO in Hashimoto's patients, coupled with elevated cholesterol, triglyceride, and low-density lipoprotein (LDL) levels. Conversely, reduced levels of high-density lipoprotein (HDL) were observed in Hashimoto’s patients compared to healthy subjects. The study establishes a noteworthy correlation between thyroid autoimmunity, thyroid disease, PLA2, hs-CRP, and lipid profile, underscoring an increased cardiovascular risk in individuals with Hashimoto’s thyroiditis. The findings emphasize the prevalence of Anti-TPO antibodies in adult Jordanian non-pregnant women with Hashimoto’s thyroiditis.
This paper is a fusion of a survey of different existing research related to web forensics, disk forensics, and email forensics and the implementation of the best practices in these areas. During the survey of ongoing state-of-the-art research, we observed that every forensic investigation process goes through five phases: identification of evidence, collection of evidence, examination of evidence, assessment/investigation of evidence, and reporting of evidence. Although phases are the same in all forensics investigations, for every forensics investigation there is a specialized set of forensics tools. This paper also highlights the need for intelligent tool selection and current challenges of web forensics, disk forensics, and email forensics and infers future research trends toward solving these current challenges. Eventually, we performed various case studies of web forensics, disk forensics, and email forensics and added three interesting investigations to this paper. The change in the price of items in the shopping cart on an e-commerce website before checkout is a case study of web forensics. To obtain system files using forensic tool kit (FTK) imager is a case study of disk forensics. Show original of g-mail is a case study of email forensics.
This paper describes a novel 4-D hyperchaotic system with a high level of complexity. It can produce chaotic, hyperchaotic, periodic, and quasi-periodic behaviors by adjusting its parameters. The study showed that the new system experienced the famous dynamical property of multistability. It can exhibit different coexisting attractors for the same parameter values. Furthermore, by using Lyapunov exponents, bifurcation diagram, equilibrium points’ stability, dissipativity, and phase plots, the study was able to investigate the dynamical features of the proposed system. The mathematical model’s feasibility is proved by applying the corresponding electronic circuit using Multisim software. The study also reveals an interesting and special feature of the system’s offset boosting control. Therefore, the new 4D system is very desirable to use in Chaos-based applications due to its hyperchaotic behavior, multistability, offset boosting property, and easily implementable electronic circuit. Then, the study presents a voice encryption scheme that employs the characteristics of the proposed hyperchaotic system to encrypt a voice signal. The new encryption system is implemented on MATLAB (R2023) to simulate the research findings. Numerous tests are used to measure the efficiency of the developed encryption system against attacks, such as histogram analysis, percent residual deviation (PRD), signal-to-noise ratio (SNR), correlation coefficient (cc), key sensitivity, and NIST randomness test. The simulation findings show how effective our proposed encryption system is and how resilient it is to different cryptographic assaults.
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Kuala Terengganu, Malaysia
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Prof Datuk Dr Ahmad Zubaidi A. Latif
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